ACM Computing Surveys (CSUR)
People tracking and segmentation using spatiotemporal shape constraints
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Robust feature descriptors for efficient vision-based tracking
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Behavior based robot localisation using stereo vision
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Measurement-based reclustering for multiple object tracking with particle filters
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Visual affect recognition
Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features
Advanced Engineering Informatics
Intelligent video surveillance system: 3-tier context-aware surveillance system with metadata
Multimedia Tools and Applications
Intrackability: Characterizing Video Statistics and Pursuing Video Representations
International Journal of Computer Vision
Symmetry-driven accumulation of local features for human characterization and re-identification
Computer Vision and Image Understanding
Soft-assigned bag of features tracking
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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We present a generic tracker which can handle a variety of different objects. For this purpose, groups of low-level features like interest points, edges, homogeneous and textured regions, are combined on a flexible and opportunistic basis. They sufficiently characterize an object and allow robust tracking as they are complementary sources of information which describe both the shape and the appearance of an object. These low-level features are integrated into a particle filter framework as this has proven very successful for non-linear and non-Gaussian estimation problems. In this paper we concentrate on rigid objects under affine transformations. Results on real-world scenes demonstrate the performance of the proposed tracker.