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
Persistent Objects Tracking Across Multiple Non Overlapping Cameras
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
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
Video sequence querying using clustering of objects' appearance models
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Activity recognition via classification constrained diffusion maps
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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In this paper, we propose a maximum a posteriori formulation to the multiple target tracking problem. We adopt a graph representation for storing the detected regions as well as their association over time. The multiple target tracking problem is formulated as a multiple paths search in the graph. Due to the noisy foreground segmentation, an object may be represented by several foreground regions and one foreground region may corresponds to multiple objects. We introduce merge, split and mean shift operations that add new hypothesis to the measurement graph in order to be able to aggregate, split detected blobs or re-acquire objects that have not been detected during stop-and-go-motion. To make full use of the visual observations, we consider both motion and appearance likelihood. Experiments have been conducted on both indoor and outdoor data sets, and a comparison has been carried to assess the contribution of the new tracker.