Tracking and data association
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
International Journal of Computer Vision
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Parallell interacting MCMC for learning of topologies of graphical models
Data Mining and Knowledge Discovery
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
Modeling and recognition of complex multi-person interactions in video
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Multiple target tracking in world coordinate with single, minimally calibrated camera
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Recent advances and trends in visual tracking: A review
Neurocomputing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Probabilistic human interaction understanding
Pattern Recognition Letters
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Human interaction dynamics are known to play an important role in the development of robust pedestrian trackers that are needed for a variety of applications in video surveillance. Traditional approaches to pedestrian tracking assume that each pedestrian walks independently and the tracker predicts the location based on an underlying motion model, such as a constant velocity or autoregressive model. Recent approaches have begun to leverage interaction, especially by modeling the repulsion forces among pedestrians to improve motion predictions. However, human interaction is more complex and is influenced by multiple social effects. This motivates the use of a more complex human interaction model for pedestrian tracking. In this paper, we propose a novel human tracking method by modeling complex social interactions. We present an algorithm that decomposes social interactions into multiple potential interaction modes. We integrate these multiple social interaction modes into an interactive Markov Chain Monte Carlo tracker and demonstrate how the developed method translates into a more informed motion prediction, resulting in robust tracking performance. We test our method on videos from unconstrained outdoor environments and evaluate it against common multi-object trackers.