Multiple view geometry in computer vision
Multiple view geometry in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust People Tracking with Global Trajectory Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
International Journal of Computer Vision
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Multi-target tracking by continuous energy minimization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Hi-index | 0.00 |
In this paper, we present an approach for tackling the problem of automatically detecting and tracking a varying number of people in complex scenes. We follow a robust and fast framework to handle unreliable detections from each camera by extensively making use of multi-camera systems to handle occlusions and ambiguities. Instead of using the detections of each frame directly for tracking, we associate and combine the detections to form so called tracklets. From the triangulation relationship between two views, the 3D trajectory is estimated and back-projected to provide valuable cues for particle filter tracking. Most importantly, a novel motion model considering different velocity cues is proposed for particle filter tracking. Experiments are done on the challenging dataset PETS'09 to show the benefits of our approach and the integrated multi-camera extensions.