A cascade classifier applied in pedestrian detection using laser and image-based features (original) (raw)
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2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011
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.
LIDAR and vision-based pedestrian detection system
Journal of Field Robotics, 2009
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.
Fusing LIDAR, camera and semantic information: A context-based approach for pedestrian detection
The International Journal of Robotics Research, 2013
In this work, a context-based multisensor system, applied for pedestrian detection in urban environment, is presented. The proposed system comprises three main processing modules: (i) a LIDAR-based module acting as primary object detection, (ii) a module which supplies the system with contextual information obtained from a semantic map of the roads, and (iii) an image-based detection module, using sliding-window detectors, with the role of validating the presence of pedestrians in regions of interest (ROIs) generated by the LIDAR module. A Bayesian strategy is used to combine information from sensors on-board the vehicle ('local' information) with information contained in a digital map of the roads ('global' information). To support experimental analysis, a multisensor dataset, named Laser and Image Pedestrian Detection dataset (LIPD), is used. The LIPD dataset was collected in an urban environment, at day light conditions, using an electrical vehicle driven at low speed. A down sampling method, using support vectors extracted from multiple linear-SVMs, was used to reduce the cardinality of the training set and, as consequence, to decrease the CPU-time during the training process of image-based classifiers. The performance of the system is evaluated, in terms of true positive rate and false positives per frame, using three image-detectors: a linear-SVM, a SVM-cascade, and a benchmark method. Additionally, experiments are performed to assess the impact of contextual information on the performance of the detection system.
Exploiting LIDAR-based features on pedestrian detection in urban scenarios
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2009
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.
A Hybrid Pedestrian Detection System based on Visible Images and LIDAR Data
Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
This paper presents a hybrid pedestrian detection system on the basis of 3D LIDAR data and visible images of the same scene. The proposed method consists of two main stages. In the first stage, the 3D LIDAR data are classified to obtain a set of clusters, which will be mapped into the visible image to get regions of interests (ROIs). The second stage classifies the ROIs (pedestrian/non pedestrian) using SVM as classifier and color based histogram of oriented gradients (HOG) together with the local self-similarity (LSS) as features. The proposed method has been tested on LIPD dataset and the results demonstrate its effectiveness.
Using targets appearance to improve pedestrian classification with a laser scanner
2008 IEEE Intelligent Vehicles Symposium, 2008
Detecting and tracking pedestrians accurately is essential to design efficient and robust collision avoidance systems. But traditional approaches to pedestrian detection and tracking in dense urban environments suffer from tracking failures and wrong classifications. We propose in this paper a system that recursively estimates the true outlines of every tracked target using a set of segments called "Appearance". Both the state and the true contours of each target are recursively estimated and can then be used for accurate classification. We show also that accurate information on target outlines allow for a meticulous occlusions computation and an enhanced data association. The performances of this new approach is assessed through a qualitative and quantitative comparison with a state of the art pedestrian detection algorithm.
Pedestrian Detection Approach for Driver Assisted System using Haar based Cascade Classifiers
International Journal of Advanced Computer Science and Applications
Object detection and tracking with the aid of computer vision is a most challenging task in the context of Driver Assistant System (DAS) for vehicles. This paper presents pedestrians detection techique using Haar-Like Features. The main aim of this research is to develop a detection system for vehicle drivers that will intimate them in advance for pedestrian's movement when they are crossing the zebra region or passing nearby to it along the road. For this purpose, dataset of 1000 images have been taken via CCTV camera which was mounted for road monitoring. A Haar based cascade classifiers have been implemented over images. And system is trained for positive (with people) and negative (without people) image samples, respectively. After testing, the obtained results show that it attained 90% accuracy while pedestrian detection. The proposed work provides significant contribution in order to reduce the road accidents as well as ensure the safety measurement for road management.
Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods
2019
The goal of this paper is to classify objects mapped by LiDAR sensor into different classes such as vehicles, pedestrians and bikers. Utilizing a LiDAR-based object detector and Neural Networks-based classifier, a novel real-time object detection is presented essentially with respect to aid self-driving vehicles in recognizing and classifying other objects encountered in the course of driving and proceed accordingly. We discuss our work using machine learning methods to tackle a common high-level problem found in machine learning applications for self-driving cars: the classification of pointcloud data obtained from a 3D LiDAR sensor.
Partially Occluded Pedestrian Classification using Three Stage Cascaded Classifier
IEEE
Pedestrian detection is an important area in com puter vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features and linear-SVM is first employed to classify the whole scanning window. For ambiguous patterns, a set of part-based classifiers trained on features derived from non-occluded dataset are employed on the second stage. Several fusion methods including average, maximum, linear and non-linear SVM classifiers are examined to combine the obtained part scores. The linear/non-linear fusion coefficients are estimated by learning an additional third stage SVM classifier. The training data in the third stage classifier is augmented by generating a set of artificially occluded samples which simulate real occlusion conditions commonly occurred in pedestrians. Experimental results using Daimler and INRIA data sets show the effectiveness of the proposed approach.
Context aided pedestrian detection for danger estimation based on laser scanner and computer vision
Expert Systems with Applications, 2014
Road safety applications demand the most reliable sensor systems. In recent years, the advances in information technologies have led to more complex road safety applications able to cope with a high variety of situations. These applications have strong sensing requirements that a single sensor, with the available technology, cannot attain. Recent researches in Intelligent Transport Systems (ITS) try to overcome the limitations of the sensors by combining them. But not only sensor information is crucial to give a good and robust representation of the road environment; context information has a key role for reliable safety applications to provide reliable detection and complete situation assessment. This paper presents a novel approach for pedestrian detection using sensor fusion of laser scanner and computer vision. The application also takes advantage of context information, providing danger estimation for the pedestrians detected. Closing the loop, the danger estimation is later used, together with context information, as feedback to enhance the pedestrian detection process.