Using a genetic algorithm for multi-hypothesis tracking

Hillis, David B.

doi:10.1109/tai.1997.632244

Cited by 20 publications

(6 citation statements)

References 16 publications

(7 reference statements)

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“…Genetic Algorithms (Gas) have been previously applied to the data association problem in the radar context by Angus et al (1993) (on a single scan scenario), and by Hillis (1997) to deal with the multi-scan data association problem. In Patricio et al (2007b), in order to achieve a fast video process, the performance of a family of very efficient evolutionary computation algorithms is analyzed.…”

Section: Heuristic Methodsmentioning

confidence: 99%

Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

et al. 2010

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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.

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Section: Heuristic Methodsmentioning

confidence: 99%

Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

et al. 2010

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“…Methods based on fuzzy systems and artificial n eural n etworks h ave been used to compute the association probabilities in JPDAF, to take the best decisions in the association process in different conditions, accordingly to the characteristics of objects and available sensors (Turkmen et al 2004;Sengupta et al 1989;Chen et al 2001). Genetic Algorithms, with a recognized capability to address hard search problems, have been previously applied in the data association problem in radar data processing by Angus et al (1993) and by Hillis (1997) to deal with the mono and multiscan data association problems, respectively. The authors have also proposed the use of evolutionary computation in visual data association .…”

Section: Sensor Data Association and Soft-computing Approachesmentioning

confidence: 99%

Fuzzy region assignment for visual tracking

et al. 2011

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In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects' features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.

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“…Genetic algorithms (GAs) have been previously applied to the data association problem in the radar context by Angus et al (1993) and Carrier, Litva, Leung, and Lo (1996) (on a single scan scenario), and by Hillis (1997) to deal with the multi-scan data association problem. In Patricio et al (2007), in order to achieve a fast video process, the performance of a family of very efficient evolutionary computation algorithms is analyzed.…”

Section: Application Of Heuristic Search Algorithmsmentioning

confidence: 99%

Discrete Optimization Algorithms in Real-Time Visual Tracking

Patricio

Dotú

García

et al. 2009

Applied Artificial Intelligence

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In this work we introduce a novel formulation of the association problem in visual tracking systems as a discrete optimization problem. The full data association problem is formulated as a search for the best tracking configuration to match hypothesis. We have implemented three local search algorithms: Hill Climbing, Simulated Annealing, and Tabu Search algorithms. These algorithms are guided by heuristic evaluation function which takes into account structural and specific information such as distance and shape. We have also introduced a novel technique in order to achieve incrementality in discrete optimization algorithms searching on an indirect space. We will show how this incrementality produces efficient and fast results, especially so when real-time is a hard constraint. The results obtained with the three discrete optimization algorithms are compared with other well-known and powerful computer vision tracking algorithms. We will prove the effectiveness and robustness of the discrete optimization algorithms in five different real-life scenarios.

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Using a genetic algorithm for multi-hypothesis tracking

Cited by 20 publications

References 16 publications

Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

Fuzzy region assignment for visual tracking

Discrete Optimization Algorithms in Real-Time Visual Tracking

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