Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Surveillance for Moving Vehicles
International Journal of Computer Vision - Special issue on a special section on visual surveillance
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Reactive Tabu Search and Sensor Selection in Active Structural Acoustic Control Problems
Journal of Heuristics
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
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Journal of Heuristics
ACM Computing Surveys (CSUR)
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Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
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EURASIP Journal on Applied Signal Processing
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Evolutionary Computation
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IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
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CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
IEEE Transactions on Evolutionary Computation
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In this paper, we present a fast and efficient technique for the data association problem applied to visual tracking systems. Visual tracking process is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, color, etc.We introduce a Tabu Search algorithm which performs a search on an indirect space. A novel problem formulation allows us to transform any solution into the real search space, which is needed for fitness calculation, in linear time. This new formulation and the use of auxiliary structures yields a fast transformation from a blob-to-track assignment space to the real shape and position of tracks space (while calculating fitness in an incremental fashion), which is key in order to produce efficient and fast results. Other previous approaches are based on statistical techniques or on evolutionary algorithms. These techniques are quite efficient and robust although they cannot converge as fast as our approach.