CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Image Registration, Optical Flow and Local Rigidity
Journal of Mathematical Imaging and Vision
A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Object Tracking with Kernel Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Robust Tracking Using Foreground-Background Texture Discrimination
International Journal of Computer Vision
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
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
UMD_VDT, an Integration of Detection and Tracking Methods for Multiple Human Tracking
Multimodal Technologies for Perception of Humans
CLEAR'07 Evaluation of USC Human Tracking System for Surveillance Videos
Multimodal Technologies for Perception of Humans
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ETISEO, performance evaluation for video surveillance systems
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiple Hypothesis Tracking Method with Fragmentation Handling
CRV '09 Proceedings of the 2009 Canadian Conference on Computer and Robot Vision
Computational Color Constancy: Survey and Experiments
IEEE Transactions on Image Processing
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We introduce a multi-target tracking algorithm that operates on prerecorded video as typically found in post-incident surveillance camera investigation. Apart from being robust to visual challenges such as occlusion and variation in camera view, our algorithm is also robust to temporal challenges, in particular unknown variation in frame rate. The complication with variation in frame rate is that it invalidates motion estimation. As such, tracking algorithms based on motion models will show decreased performance. On the other hand, appearance based detection in individual frames suffers from a plethora of false detections. Our tracking algorithm, albeit relying on appearance based detection, deals robustly with the caveats of both approaches. The solution rests on the fact that for prerecorded video we can make fully informed choices; not only based on preceding, but also based on following frames. We start off from an appearance based object detection algorithm able to detect in each frame all target objects. From this we build a graph structure. The detections form the graph's nodes and the vertices are formed by connecting each detection in a frame to all detections in the following frame. Thus, each path through the graph shows some particular selection of successive detections. Tracking is then reformulated as a heuristic search for optimal paths, where optimal means to find all detections belonging to a single object and excluding any other detection. We show that this approach, without an explicit motion model, is robust to both the visual and temporal challenges.