A Unified Framework for Tracking through Occlusions and across Sensor Gaps
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Spatio-Temporal Context for Robust Multitarget Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blind inverse gamma correction
IEEE Transactions on Image Processing
A scheme for the detection and tracking of people tuned for aerial image sequences
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Alignment of velocity fields for video surveillance
Pattern Recognition Letters
Activity recognition in wide aerial video surveillance using entity relationship models
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
International Journal of Computer Vision
| Hi-index | 0.00 |
In this paper, we tackle the problem of object detection and tracking in a new and challenging domain of wide area surveillance. This problem poses several challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data and low framerate of video. We propose a method that overcomes these challenges and evaluate it on CLIF dataset. We use median background modeling which requires few frames to obtain a workable model. We remove false detections due to parallax and registration errors using gradient information of the background image. In order to keep complexity of the tracking problem manageable, we divide the scene into grid cells, solve the tracking problem optimally within each cell using bipartite graph matching and then link tracks across cells. Besides tractability, grid cells allow us to define a set of local scene constraints such as road orientation and object context. We use these constraints as part of cost function to solve the tracking problem which allows us to track fast-moving objects in low framerate videos. In addition to that, we manually generated groundtruth for four sequences and performed quantitative evaluation of the proposed algorithm.