Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Multiple Human Objects Tracking in Crowded Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Multi-mode Target Tracking on a Crowd Scene
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate appearance-based Bayesian tracking for maneuvering targets
Computer Vision and Image Understanding
Traffic Abnormality Detection through Directional Motion Behavior Map
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Counting People in Crowded Environments by Fusion of Shape and Motion Information
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Crowd Counting Using Group Tracking and Local Features
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Online detection of unusual events in videos via dynamic sparse coding
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust tracking using local sparse appearance model and K-selection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Hi-index | 0.10 |
A multi-part sparse representation method is used in random crowded scenes for pedestrian tracking in this paper. In crowded scenes, there are random movements and orderly movements. Random movements are defined as the motion of each individual in the crowd appears to be unique, and different participants move in different directions over time. This means methods about multi-model in motion flows are not available. As a result, we propose a fully unsupervised tracking algorithm based on a multi-part local sparse appearance model. Based on the facts that only non-occluded segments of a target are effective in feature matching, while the occluded segments are the disturbed ones, our algorithm employs a multi-part sparse reconstruction code. The method is used on target segments in stead of the whole target, and implemented by solving an l"1 regularized least squares problem. The segment group with the smallest projection error will be taken as the tracking result. All the segment groups are drawn based on a density distribution in a Bayesian state inference framework. After tracking process in each frame, the template dictionary will be jointly inferred and updated to adapt appearance variation. We test the method on numerous videos including different type of very crowded scenes with serious occlusion and illumination variation. The proposed approach demonstrates excellent performance in comparison with previous methods.