Alternative tilings for improved surface area estimates by local counting algorithms
Computer Vision and Image Understanding
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Discrete Applied Mathematics
Multi View Image Surveillance and Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
Hexagonal Image Processing: A Practical Approach (Advances in Pattern Recognition)
Hexagonal Image Processing: A Practical Approach (Advances in Pattern Recognition)
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
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
People tracking algorithm for human height mounted cameras
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
A large margin framework for single camera offline tracking with hybrid cues
Computer Vision and Image Understanding
A multi-view annotation tool for people detection evaluation
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Exploiting pedestrian interaction via global optimization and social behaviors
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Online multi-target tracking by large margin structured learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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We propose a global optimisation approach to multi-target tracking. The method extends recent work which casts tracking as an integer linear program, by discretising the space of target locations. Our main contribution is to show how dynamic models can be integrated in such an approach. The dynamic model, which encodes prior expectations about object motion, has been an important component of tracking systems for a long time, but has recently been dropped to achieve globally optimisable objective functions. We re-introduce it by formulating the optimisation problem such that deviations from the prior can be measured independently for each variable. Furthermore, we propose to sample the location space on a hexagonal lattice to achieve smoother, more accurate trajectories in spite of the discrete setting. Finally, we argue that non-maxima suppression in the measured evidence should be performed during tracking, when the temporal context and the motion prior are available, rather than as a preprocessing step on a per-frame basis. Experiments on five different recent benchmark sequences demonstrate the validity of our approach.