Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling (original) (raw)
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Part-based multiple-person tracking with partial occlusion handling
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012
Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. In this paper, we address such problems by proposing a robust part-based tracking-by-detection framework. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Our approach learns part-based person-specific SVM classifiers which capture the articulations of the human bodies in dynamically changing appearance and background. With the part-based model, our approach is able to handle partial occlusions in both the detection and the tracking stages. In the detection stage, we select the subset of parts which maximizes the probability of detection, which significantly improves the detection performance in crowded scenes. In the tracking stage, we dynamically handle occlusions by distributing the score of the learned person classifier among its corresponding parts, which allows us to detect and predict partial occlusions, and prevent the performance of the classifiers from being degraded. Extensive experiments using the proposed method on several challenging sequences demonstrate state-of-the-art performance in multiple-people tracking.
Tracking Multiple People Under Occlusion Using MultipleCameras
Procedings of the British Machine Vision Conference 2000, 2000
We describe a system for tracking multiple people with multiple cameras based on fusion of multiple cues. Face trackers are used to self-calibrate our system. Epipolar geometry and landmarks are employed to disambiguate the tracking problem. The correlation of visual information between different cameras is learnt using Support Vector Regression and Hierarchical Principal Component Analysis to estimate the subject appearance across cameras. The joint features of subjects extracted from multiple cameras are tracked and used as a model to re-track people once the subjects are lost tracking in the system. Results demonstrate that our system can deal with the occlusion.
International Journal of …, 2011
The proposed approach aims to track multiple moving people in a colour video acquired with a single camera. The first phase of the approach consists in precisely detecting multi-human inside moving foregrounds. The input to this phase is foreground pixels which were extracted from the scene using any background subtraction technique. These moving foregrounds are then further segmented into multiple moving people using region segmentation and shape-based occlusion handling. The second phase assigns the detected human blobs to tracks using robust matching process based both on appearance model and motion model. For this, we use Kalman filter to predict future locations and sizes for dynamic persons and fuse this information with appearance-based comparison in order to assign each blob to a track. The preliminary experiments on several representative sequences have shown that this unsupervised approach can robustly detect and track multiple occluded moving persons, even at lower temporal resolution.
Multiple Objects Tracking Under Occlusions: A Survey
long term occlusion is a most important challenge in any multiple objects tracking system. This paper presents a literature survey of an object tracking algorithms in a fixed camera situation that have been used by others to address the long term occlusion problem. Based on this assessment of the state of the art, this paper identify what appears to be the most promising algorithm for long term occlusion that was succeeds in handling interacting objects of similar appearance without any strong assumptions on the characteristics of the tracked objects. But this algorithm failed to handle objects of too complex shapes and appearance, and the tracking results was affected by successfully of background subtraction. This paper presents a proposed solution to these failed points.
Person tracking with partial occlusion handling
2015 IEEE Workshop on Signal Processing Systems (SiPS), 2015
Occlusion is a challenge for tracking especially in dynamic scene. It adds the consideration for background modeling. In the condition, the tracker will be influenced by both occlusions and background. In this paper, we address the problem by proposing a robust algorithm based on improved particle filter using discriminative model without background modeling. Discriminative model offers accurate templates for occlusion detection by alleviating influence from background pixels. Since particle filter cannot carry out effective tracking under heavy occlusion, blocking is introduced to solve the problem by abandoning unobservable parts of the target. Experimental results show that our algorithm can work persistently and effectively under severe occlusion even in dynamic scene compared with state-of-the-arts.
Occlusion model from human interaction analysis for tracking multiple people
Computer Graphics and Imaging / 798: Signal Processing, Pattern Recognition and Applications, 2013
In this work we investigate the problem of tracking multiple interacting people under uncontrolled stationary environments for intelligent surveillance applications. This domain is very challenging since the clothing appearance changes of the people over time make difficult the temporal association of their identities. The problem is emphasized when individuals move close to each other, are occluded, or abruptly change their trajectories. We propose a tracking graph that models spatial and temporal relationships among people in order to predict and resolve partial and total occlusions. When a total occlusion event occurs, the model generates three possible hypotheses about the location of the occluded person according to a human interaction analysis. This model is able to detect false positives and false negatives in the detection measurements and it can also estimate the location of missing or occluded people. Our approach was evaluated on benchmark sequences and results show how it outperforms state-of-the-art algorithms even in the presence of long periods of occlusion.
A hierarchical model of dynamics for tracking people with a single video camera
2000
We propose a novel hierarchical model of human dynamics for view independent tracking of the human body in monocular video sequences. The model is trained using real data from a collection of people. Kinematics are encoded using Hierarchical Principal Component Analysis, and dynamics are encoded using Hidden Markov Models. The top of the hierarchy contains information about the whole body. The lower levels of the hierarchy contain more detailed information about possible poses of some subpart of the body. When tracking, the lower levels of the hierarchy are shown to improve accuracy. In this article we describe our model and present experiments that show we can recover 3D skeletons from 2D images in a view independent manner, and also track people the system was not trained on.
Robust multi-person tracking from moving platforms
2008
In this paper, we address the problem of multi-person tracking in busy pedestrian zones, using a stereo rig mounted on a mobile platform. The complexity of the problem calls for an integrated solution, which extracts as much visual information as possible and combines it through cognitive feedback. We propose such an approach, which jointly estimates camera position, stereo depth, object detection, and tracking. We model the interplay between these components using a graphical model. Since the model has to incorporate object-object interactions, and temporal links to past frames, direct inference is intractable. We therefore propose a two-stage procedure: for each frame we first solve a simplified version of the model (disregarding interactions and temporal continuity) to estimate the scene geometry and an overcomplete set of object detections. Conditioned on these results, we then address object interactions, tracking, and prediction in a second step. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of realistic complexity.
Tracking multiple people with recovery from partial and total occlusion
Pattern Recognition, 2005
Robust tracking of multiple people in video sequences is a challenging task. In this paper, we present an algorithm for tracking faces of multiple people even in cases of total occlusion. Faces are detected first; then a model for each person is built. The models are handed over to the tracking module which is based on the mean shift algorithm, where each face is represented by the non-parametric distribution of the colors in the face region. The mean shift tracking algorithm is robust to partial occlusion and rotation, and is computationally efficient, but it does not deal with the problem of total occlusion. Our algorithm overcomes this problem by detecting the occlusion using an occlusion grid, and uses a non-parametric distribution of the color of the occluded person's cloth to distinguish that person after the occlusion ends. Our algorithm uses the speed and the trajectory of each occluded person to predict the locations that should be searched after occlusion ends. It integrates multiple features to handle tracking multiple people in cases of partial and total occlusion. Experiments on a large set of video clips demonstrate the robustness of the algorithm, and its capability to correctly track multiple people even when faces are temporarily occluded by other faces or by other objects in the scene.
Tracking of persons for video surveillance of unattended environments
2008 19th International Conference on Pattern Recognition, 2008
This paper describes a visual surveillance system for remote monitoring of unattended environments. For the purpose of efficiently tracking multiple people in the presence of occlusions, we propose: (i) to combine blob matching with particle filtering, and (ii) to augment these tracking algorithms with a novel colour appearance model. The proposed system efficiently counteracts the shortcomings of the two algorithms by switching from one to the other during occlusions. Results on public datasets as well as real surveillance videos from a metropolitan railway station demonstrate the efficacy of the proposed system.