Tracking and data association
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting the effectiveness of Naïve data fusion on the basis of system characteristics
Journal of the American Society for Information Science
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint Probabilistic Techniques for Tracking Multi-Part Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Adaptive Fusion Architecture for Target Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Probabilistic model-based multisensor image fusion
Probabilistic model-based multisensor image fusion
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval
Information Retrieval
A Dynamic Pruning and Feature Selection Strategy for Real-Time Tracking
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mixed group ranks: preference and confidence in classifier combination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Target tracking for mobile robot platforms via object matching and background anti-matching
Robotics and Autonomous Systems
Rank-score characteristics (RSC) function and cognitive diversity
BI'10 Proceedings of the 2010 international conference on Brain informatics
AMT'11 Proceedings of the 7th international conference on Active media technology
BI'11 Proceedings of the 2011 international conference on Brain informatics
AMT'12 Proceedings of the 8th international conference on Active Media Technology
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Video target tracking is the process of estimating the current state, and predicting the future state of a target from a sequence of video sensor measurements. Multitarget video tracking is complicated by the fact that targets can occlude one another, affecting video feature measurements in a highly non-linear and difficult to model fashion. In this paper, we apply a multisensory fusion approach to the problem of multitarget video tracking with occlusion. The approach is based on a data-driven method (CFA) to selecting the features and fusion operations that improve a performance criterion. Each sensory cue is treated as a scoring system. Scoring behavior is characterized by a rank-score function. A diversity measure, based on the variation in rank-score functions, is used to dynamically select the scoring systems and fusion operations that produce the best tracking performance. The relationship between the diversity measure and the tracking accuracy of two fusion operations, a linear score combination and an average rank combination, is evaluated on a set of 12 video sequences. These results demonstrate that using the rank-score characteristic as a diversity measure is an effective method to dynamically select scoring systems and fusion operations that improve the performance of multitarget video tracking with occlusions.