Cristiano Premebida | University of Coimbra (original) (raw)

Papers by Cristiano Premebida

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011

Pedestrian detection systems constitute an important field of research and development in compute... more Pedestrian detection systems constitute an important field of research and development in computer vision, specially when applied in protection/safety systems in urban scenarios due to their direct impact in the society, specifically in terms of traffic casualties. In order to face such challenge, this work exploits some developments on statistical machine learning theory, in particular structural risk minimization (SRM) in a cascade ensemble. Namely, the ensemble applies the principle of SRM on a set of linear support vector machines (SVM). The linear SVM complexity, in the Vapnik sense, is controlled by choosing the dimension of the feature space in each cascade stage. To support experimental analysis, a multisensor dataset constituted by data from a LIDAR, a monocular camera, an IMU, encoder and a DGPS is introduced in this paper. The dataset, named Laser and Image Pedestrian Detection (LIPD) dataset, was collected in an urban environment, at day light conditions, using an electrical vehicle driven at low speed. Labeled pedestrians and non-pedestrians samples are also available for benchmarking purpose. The cascade of SVMs, trained with image-based features (HOG and COV descriptors), is used to detect pedestrian evidences on regions of interest (ROI) generated by a LIDAR-based processing system. Finally, the paper presents experimental results comparing the performance of a Boosting-SVM cascade and the proposed SRM-SVM cascade classifiers, in terms of detection errors.

… , 2007. ITSC 2007. …, Jan 1, 2007

This paper presents a sensorial-cooperative architecture to detect, track and classify entities i... more This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by a Lidar and a monocular camera is used in the here proposed system. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2010

In this paper we present a multistage method applied in pedestrian detection using information fr... more In this paper we present a multistage method applied in pedestrian detection using information from a LIDAR and a monocular-camera mounted on an electric vehicle driving in urban scenarios. The proposed method is a cascade of classifiers trained in two subsets of features, one with laserbased features and the other with a set of image-based features. A specific training approach was developed to adjust the cascade stages in order to enhance the classification performance. The proposed method differs from the conventional cascade regarding the way the selected samples are propagated through the cascade. Thus, the subsequent stages of the proposed cascade receive both negatives and positives from previous ones, relying on a decision margin process. Experiments were conducted in off-line mode, for a set of single component classifiers and for the proposed cascade technique. The results are compared in terms of classification performance metrics and ROC curves.

Journal of Field Robotics, 2009

A perception system for pedestrian detection in urban scenarios using information from a LIDAR an... more A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor-feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false-alarms under tolerable values in comparison with singlebased classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies.

ABSTRACT This work explores three schemes for pedestrian detection in urban scenarios using infor... more ABSTRACT This work explores three schemes for pedestrian detection in urban scenarios using information gathered by a LIDAR and a monocular camera mounted on an electric vehicle. In the first scheme, pedestrian detection is conduct by a set of single classification methods trained with LIDAR and/or vision-based features. In the second scheme, the likelihoods from the single-classifiers are fused by means of three fusion rules: average, maximum value and a Naive-product rule. Finally, the third scheme is a cascade of classifiers with four-stages, two per feature space. All these pedestrian detection strategies were compared through off-line experiments con-ducted using our dataset, that is available on the Web for public usage.

Proc. of the IEEE/RSJ …, Jan 1, 2006

A multi-module architecture to detect, track and classify objects in semi-structured outdoor scen... more A multi-module architecture to detect, track and classify objects in semi-structured outdoor scenarios for intelligent vehicles is proposed in this paper. In order to fulfill this task it was used the information provided by a laser range finder (LRF) and a monocular camera. The detection and tracking phases are performed in the LRF space, and the object classification methods work both in laser (with a Majority Voting scheme and a Gaussian Mixture Model (GMM) classifier) and in vision spaces (AdaBoost classifier). A sum decision rule based on the Bayes approach is used in order to combine the results of each classification technique, and hence a more reliable object classification is achieved. Experiments using real data confirm the robustness of the proposed architecture.

The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014

In this paper some algorithms for 2D segmentation, feature detection and fitting are presented. T... more In this paper some algorithms for 2D segmentation, feature detection and fitting are presented. The features discussed here consist of three geometric primitives: lines, circles and ellipses. The segmentation process, whose objective is grouping segments that belong to the same object, is analysed using several kinds of algorithms. Results are presented using real data scanned by a laser range finder

2006 IEEE Intelligent Transportation Systems Conference, 2006

Intelligent vehicles need reliable information about the environment in order to operate with tot... more Intelligent vehicles need reliable information about the environment in order to operate with total safety. In this paper we propose a flexible multi-module architecture for a Multi-Target Detection and Tracking System (MTDTS) complemented with a Bayesian object Classification layer based on finite Gaussian Mixture Models (GMM). The GMM parameters are estimated by an Expectation Maximization (EM) algorithm, hence finite-component models were generated based on feature-vectors extracted from object's classes during the training stage. Using the joint mixture Gaussian pdf modelled for each class, a Bayesian approach is used to distinct the object's categories (persons, tree-trunks/posts, and cars) in a semi-structured outdoor environment based on data from a laser range finder (LRF). Experiments using real data scan confirm the robustness of the proposed architecture. This paper investigates a particular problem: detection, tracking and classification of objects in cybercars-like outdoor environments.

2014 IEEE Vehicle Power and Propulsion Conference (VPPC), 2014

In this work, we propose an approach that relies on cues from depth perception from RGB-D images,... more In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier likelihoods into a single form, assigning weights (by an uncertainty measure) to counterbalance the likelihoods as a posterior probability. Temporal information is incorporated in the DBMM by means of prior probabilities, taking into consideration previous probabilistic inference to reinforce current-frame classification. The publicly available Cornell Activity Dataset [1] with 12 different human activities was used to evaluate the proposed approach. Reported results on testing dataset show that our approach overcomes state of the art methods in terms of precision, recall and overall accuracy. The developed work allows the use of activities classification for applications where the human behaviour recognition is important, such as human-robot interaction, assisted living for elderly care, among others.

IEEE Transactions on Vehicular Technology, 2014

This work presents a method for simultaneous segmentation and modeling of objects detected in ran... more This work presents a method for simultaneous segmentation and modeling of objects detected in range data gathered by a laserscanner mounted onboard groundrobotic platforms. Superquadrics are used as model for both segmentation and object shape fitting. The proposed method, which we name Simultaneous Segmentation and Superquadrics Fitting (S3F), relies on a novel global objective function which accounts for the size of the object, the distance of range points, and for partial-occlusions. Experimental results, using 2D range data collected from indoor and outdoor environments, are qualitatively and quantitatively analyzed. Results are compared with popular and state-of-the-art segmentation methods. Moreover, we present results using 3D data obtained from an in house setup, and also from a Velodyne LIDAR. This work finds applications in areas of mobile robotics and autonomous vehicles, namely object detection, segmentation and modeling.

2014 12th IEEE International Conference on Industrial Informatics (INDIN), 2014

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2009

Reliable detection and classification of vulnerable road users constitute a critical issue on saf... more Reliable detection and classification of vulnerable road users constitute a critical issue on safety/protection systems for intelligent vehicles driving in urban zones. In this subject, most of the perception systems have LIDAR and/or Radar as primary detection modules and vision-based systems for object classification. This work, on the other hand, presents a valuable analysis of pedestrian detection in urban scenario using exclusively LIDAR-based features. The aim is to explore how much information can be extracted from LIDAR sensors for pedestrian detection. Moreover, this study will be useful to compose multi-sensor based pedestrian detection systems using not only LIDAR but also vision sensors. Experimental results using our data set and a detailed classification performance analysis are presented, with comparisons among various classification techniques.

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2010

In this paper we present a multistage method applied in pedestrian detection using information fr... more In this paper we present a multistage method applied in pedestrian detection using information from a LIDAR and a monocular-camera mounted on an electric vehicle driving in urban scenarios. The proposed method is a cascade of classifiers trained in two subsets of features, one with laserbased features and the other with a set of image-based features. A specific training approach was developed to adjust the cascade stages in order to enhance the classification performance. The proposed method differs from the conventional cascade regarding the way the selected samples are propagated through the cascade. Thus, the subsequent stages of the proposed cascade receive both negatives and positives from previous ones, relying on a decision margin process. Experiments were conducted in off-line mode, for a set of single component classifiers and for the proposed cascade technique. The results are compared in terms of classification performance metrics and ROC curves.

IEEE International Conference on Intelligent Robots and Systems, 2012

Many robotic systems combine cameras with Laser Rangefinders (LRF) for simultaneously achieving m... more Many robotic systems combine cameras with Laser Rangefinders (LRF) for simultaneously achieving multipurpose visual sensing and accurate depth recovery. Employing a single sensor modality for accomplishing both goals is an appealing proposition because it enables substantial savings in equipment, and tends to decrease the overall complexity of the system. This article explores the possibility of replacing LRF by passive stereo vision for reconstructing the scene along a 2D scan plane. We present a new stereo algorithm that is specifically tailored for the purpose. The algorithm recovers the depth along the scan plane using a symmetry-based matching cost (SymStereo), and refines the raw estimates by applying dynamic programming, followed by a Markov Random Field (MRF) that decides if the reconstructed contour is a line or not. We report for the first time comparative experiments between Stereo Rangefinding (SRF) and LRF. The results are encouraging by showing that SRF can be a plausible alternative to LRF in several application scenarios. Moreover, since SRF also enables independent depth estimates along multiple scan planes with arbitrary orientation, being the only constraint that the scan plane intersects the stereo baseline, it is an important benefit that can be decisive for many robotic applications.

Integrated Computer-Aided Engineering, 2013

This paper presents an experimental study on pedestrian classification and detection in far infra... more This paper presents an experimental study on pedestrian classification and detection in far infrared (FIR) images. The study includes an in-depth evaluation of several combinations of features and classifiers, which include features previously used for daylight scenarios, as well as a new descriptor (HOPE -Histograms of Oriented Phase Energy), specifically targeted to infrared images, and a new adaptation of a latent variable SVM approach to FIR images. The presented results are validated on a new classification and detection dataset of FIR images collected in outdoor environments from a moving vehicle. The classification space contains 16152 pedestrians and 65440 background samples evenly selected from several sequences acquired at different temperatures and different illumination conditions. The detection dataset consist on 15224 images with ground truth information. The authors are making this dataset public for benchmarking new detectors in the area of intelligent vehicles and field robotics applications.

2007 IEEE Intelligent Transportation Systems Conference, 2007

This paper presents a sensorial-cooperative architecture to detect, track and classify entities i... more This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by a Lidar and a monocular camera is used in the here proposed system. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011

Pedestrian detection systems constitute an important field of research and development in compute... more Pedestrian detection systems constitute an important field of research and development in computer vision, specially when applied in protection/safety systems in urban scenarios due to their direct impact in the society, specifically in terms of traffic casualties. In order to face such challenge, this work exploits some developments on statistical machine learning theory, in particular structural risk minimization (SRM) in a cascade ensemble. Namely, the ensemble applies the principle of SRM on a set of linear support vector machines (SVM). The linear SVM complexity, in the Vapnik sense, is controlled by choosing the dimension of the feature space in each cascade stage. To support experimental analysis, a multisensor dataset constituted by data from a LIDAR, a monocular camera, an IMU, encoder and a DGPS is introduced in this paper. The dataset, named Laser and Image Pedestrian Detection (LIPD) dataset, was collected in an urban environment, at day light conditions, using an electrical vehicle driven at low speed. Labeled pedestrians and non-pedestrians samples are also available for benchmarking purpose. The cascade of SVMs, trained with image-based features (HOG and COV descriptors), is used to detect pedestrian evidences on regions of interest (ROI) generated by a LIDAR-based processing system. Finally, the paper presents experimental results comparing the performance of a Boosting-SVM cascade and the proposed SRM-SVM cascade classifiers, in terms of detection errors.

… , 2007. ITSC 2007. …, Jan 1, 2007

This paper presents a sensorial-cooperative architecture to detect, track and classify entities i... more This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by a Lidar and a monocular camera is used in the here proposed system. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2010

In this paper we present a multistage method applied in pedestrian detection using information fr... more In this paper we present a multistage method applied in pedestrian detection using information from a LIDAR and a monocular-camera mounted on an electric vehicle driving in urban scenarios. The proposed method is a cascade of classifiers trained in two subsets of features, one with laserbased features and the other with a set of image-based features. A specific training approach was developed to adjust the cascade stages in order to enhance the classification performance. The proposed method differs from the conventional cascade regarding the way the selected samples are propagated through the cascade. Thus, the subsequent stages of the proposed cascade receive both negatives and positives from previous ones, relying on a decision margin process. Experiments were conducted in off-line mode, for a set of single component classifiers and for the proposed cascade technique. The results are compared in terms of classification performance metrics and ROC curves.

Journal of Field Robotics, 2009

A perception system for pedestrian detection in urban scenarios using information from a LIDAR an... more A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor-feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false-alarms under tolerable values in comparison with singlebased classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies.

ABSTRACT This work explores three schemes for pedestrian detection in urban scenarios using infor... more ABSTRACT This work explores three schemes for pedestrian detection in urban scenarios using information gathered by a LIDAR and a monocular camera mounted on an electric vehicle. In the first scheme, pedestrian detection is conduct by a set of single classification methods trained with LIDAR and/or vision-based features. In the second scheme, the likelihoods from the single-classifiers are fused by means of three fusion rules: average, maximum value and a Naive-product rule. Finally, the third scheme is a cascade of classifiers with four-stages, two per feature space. All these pedestrian detection strategies were compared through off-line experiments con-ducted using our dataset, that is available on the Web for public usage.

Proc. of the IEEE/RSJ …, Jan 1, 2006

A multi-module architecture to detect, track and classify objects in semi-structured outdoor scen... more A multi-module architecture to detect, track and classify objects in semi-structured outdoor scenarios for intelligent vehicles is proposed in this paper. In order to fulfill this task it was used the information provided by a laser range finder (LRF) and a monocular camera. The detection and tracking phases are performed in the LRF space, and the object classification methods work both in laser (with a Majority Voting scheme and a Gaussian Mixture Model (GMM) classifier) and in vision spaces (AdaBoost classifier). A sum decision rule based on the Bayes approach is used in order to combine the results of each classification technique, and hence a more reliable object classification is achieved. Experiments using real data confirm the robustness of the proposed architecture.

The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014

In this paper some algorithms for 2D segmentation, feature detection and fitting are presented. T... more In this paper some algorithms for 2D segmentation, feature detection and fitting are presented. The features discussed here consist of three geometric primitives: lines, circles and ellipses. The segmentation process, whose objective is grouping segments that belong to the same object, is analysed using several kinds of algorithms. Results are presented using real data scanned by a laser range finder

2006 IEEE Intelligent Transportation Systems Conference, 2006

Intelligent vehicles need reliable information about the environment in order to operate with tot... more Intelligent vehicles need reliable information about the environment in order to operate with total safety. In this paper we propose a flexible multi-module architecture for a Multi-Target Detection and Tracking System (MTDTS) complemented with a Bayesian object Classification layer based on finite Gaussian Mixture Models (GMM). The GMM parameters are estimated by an Expectation Maximization (EM) algorithm, hence finite-component models were generated based on feature-vectors extracted from object's classes during the training stage. Using the joint mixture Gaussian pdf modelled for each class, a Bayesian approach is used to distinct the object's categories (persons, tree-trunks/posts, and cars) in a semi-structured outdoor environment based on data from a laser range finder (LRF). Experiments using real data scan confirm the robustness of the proposed architecture. This paper investigates a particular problem: detection, tracking and classification of objects in cybercars-like outdoor environments.

2014 IEEE Vehicle Power and Propulsion Conference (VPPC), 2014

In this work, we propose an approach that relies on cues from depth perception from RGB-D images,... more In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier likelihoods into a single form, assigning weights (by an uncertainty measure) to counterbalance the likelihoods as a posterior probability. Temporal information is incorporated in the DBMM by means of prior probabilities, taking into consideration previous probabilistic inference to reinforce current-frame classification. The publicly available Cornell Activity Dataset [1] with 12 different human activities was used to evaluate the proposed approach. Reported results on testing dataset show that our approach overcomes state of the art methods in terms of precision, recall and overall accuracy. The developed work allows the use of activities classification for applications where the human behaviour recognition is important, such as human-robot interaction, assisted living for elderly care, among others.

IEEE Transactions on Vehicular Technology, 2014

This work presents a method for simultaneous segmentation and modeling of objects detected in ran... more This work presents a method for simultaneous segmentation and modeling of objects detected in range data gathered by a laserscanner mounted onboard groundrobotic platforms. Superquadrics are used as model for both segmentation and object shape fitting. The proposed method, which we name Simultaneous Segmentation and Superquadrics Fitting (S3F), relies on a novel global objective function which accounts for the size of the object, the distance of range points, and for partial-occlusions. Experimental results, using 2D range data collected from indoor and outdoor environments, are qualitatively and quantitatively analyzed. Results are compared with popular and state-of-the-art segmentation methods. Moreover, we present results using 3D data obtained from an in house setup, and also from a Velodyne LIDAR. This work finds applications in areas of mobile robotics and autonomous vehicles, namely object detection, segmentation and modeling.

2014 12th IEEE International Conference on Industrial Informatics (INDIN), 2014

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2009

Reliable detection and classification of vulnerable road users constitute a critical issue on saf... more Reliable detection and classification of vulnerable road users constitute a critical issue on safety/protection systems for intelligent vehicles driving in urban zones. In this subject, most of the perception systems have LIDAR and/or Radar as primary detection modules and vision-based systems for object classification. This work, on the other hand, presents a valuable analysis of pedestrian detection in urban scenario using exclusively LIDAR-based features. The aim is to explore how much information can be extracted from LIDAR sensors for pedestrian detection. Moreover, this study will be useful to compose multi-sensor based pedestrian detection systems using not only LIDAR but also vision sensors. Experimental results using our data set and a detailed classification performance analysis are presented, with comparisons among various classification techniques.

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2010

In this paper we present a multistage method applied in pedestrian detection using information fr... more In this paper we present a multistage method applied in pedestrian detection using information from a LIDAR and a monocular-camera mounted on an electric vehicle driving in urban scenarios. The proposed method is a cascade of classifiers trained in two subsets of features, one with laserbased features and the other with a set of image-based features. A specific training approach was developed to adjust the cascade stages in order to enhance the classification performance. The proposed method differs from the conventional cascade regarding the way the selected samples are propagated through the cascade. Thus, the subsequent stages of the proposed cascade receive both negatives and positives from previous ones, relying on a decision margin process. Experiments were conducted in off-line mode, for a set of single component classifiers and for the proposed cascade technique. The results are compared in terms of classification performance metrics and ROC curves.

IEEE International Conference on Intelligent Robots and Systems, 2012

Many robotic systems combine cameras with Laser Rangefinders (LRF) for simultaneously achieving m... more Many robotic systems combine cameras with Laser Rangefinders (LRF) for simultaneously achieving multipurpose visual sensing and accurate depth recovery. Employing a single sensor modality for accomplishing both goals is an appealing proposition because it enables substantial savings in equipment, and tends to decrease the overall complexity of the system. This article explores the possibility of replacing LRF by passive stereo vision for reconstructing the scene along a 2D scan plane. We present a new stereo algorithm that is specifically tailored for the purpose. The algorithm recovers the depth along the scan plane using a symmetry-based matching cost (SymStereo), and refines the raw estimates by applying dynamic programming, followed by a Markov Random Field (MRF) that decides if the reconstructed contour is a line or not. We report for the first time comparative experiments between Stereo Rangefinding (SRF) and LRF. The results are encouraging by showing that SRF can be a plausible alternative to LRF in several application scenarios. Moreover, since SRF also enables independent depth estimates along multiple scan planes with arbitrary orientation, being the only constraint that the scan plane intersects the stereo baseline, it is an important benefit that can be decisive for many robotic applications.

Integrated Computer-Aided Engineering, 2013

This paper presents an experimental study on pedestrian classification and detection in far infra... more This paper presents an experimental study on pedestrian classification and detection in far infrared (FIR) images. The study includes an in-depth evaluation of several combinations of features and classifiers, which include features previously used for daylight scenarios, as well as a new descriptor (HOPE -Histograms of Oriented Phase Energy), specifically targeted to infrared images, and a new adaptation of a latent variable SVM approach to FIR images. The presented results are validated on a new classification and detection dataset of FIR images collected in outdoor environments from a moving vehicle. The classification space contains 16152 pedestrians and 65440 background samples evenly selected from several sequences acquired at different temperatures and different illumination conditions. The detection dataset consist on 15224 images with ground truth information. The authors are making this dataset public for benchmarking new detectors in the area of intelligent vehicles and field robotics applications.

2007 IEEE Intelligent Transportation Systems Conference, 2007

This paper presents a sensorial-cooperative architecture to detect, track and classify entities i... more This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by a Lidar and a monocular camera is used in the here proposed system. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014