CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Multi-Object Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Particle Video: Long-Range Motion Estimation using Point Trajectories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A hybrid blob- and appearance-based framework for multi-object tracking through complex occlusions
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
CamShift guided particle filter for visual tracking
Pattern Recognition Letters
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local-motion-based probabilistic model for visual tracking
Pattern Recognition
Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple objects tracking in the presence of long-term occlusions
Computer Vision and Image Understanding
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
Tracking with Occlusions via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Tracking and Vehicle Classification via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Multiple-target tracking in complex scenes is one of the most complicated problems in computer vision. Handling the occlusion between objects is the key issue in multiple-target tracking. This paper introduces the method of motion segmentation into the object tracking system, and presents a SPA (Skeleton Points Assign, SPA) based occlusion segmentation approach to track multiple people through complex situations captured by static monocular cameras. In the proposed method, we first select the skeleton points and evaluate their occlusion states by low-level information like optical flow; then we assign these points to different objects using advanced semantic information, such as appearance, motion and color; finally, a dense classification of foreground pixels are taken advantages of to accomplish occlusion segmentation and a blob-based compensation strategy is utilized to estimate the missing information of occluded objects. Object tracking is handled by a particle filter-based tracking framework, in which a probabilistic appearance model is used to find the best particle. Experiments are conducted on the public challenging dataset PETS 2009. Results show that this approach can improve the performance of the existing tracking approach and handle dynamic occlusions better.