Tracking and data association
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Split and Merge Data Association Filter for Dense Multi-target Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Object Reacquisition Using Invariant Appearance Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
ACM Computing Surveys (CSUR)
Continuous tracking within and across camera streams
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Tracking appearances with occlusions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Tracking in a cluttered environment with probabilistic data association
Automatica (Journal of IFAC)
Semantic web technologies for video surveillance metadata
Multimedia Tools and Applications
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In this paper, we address the multiple target tracking problem as a maximum a posteriori problem. We adopt a graph representation of all observations over time. To make full use of the visual observations from the image sequence, we introduce both motion and appearance likelihood. The multiple target tracking problem is formulated as finding multiple optimal paths in the graph. Due to the noisy foreground segmentation, an object may be represented by several foreground regions and similarly one foreground region may correspond to multiple objects. To deal with this problem, we propose merge, split and mean shift operations to generate new hypotheses to the measurement graph. The proposed approach uses a sliding window framework, that aggregates information across a fixed number of frames. Experimental results on both indoor and outdoor data sets are reported. Furthermore, we provide a comparison between the proposed approach with the existing methods that do not merge/split detected blobs.