Sustained observability for salient motion detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Computers & Mathematics with Applications
A hybrid motion and appearance prediction model for robust visual object tracking
Pattern Recognition Letters
Weighted attentional blocks for probabilistic object tracking
The Visual Computer: International Journal of Computer Graphics
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We present a novel tracking method for effectively tracking objects in structured environments. The tracking method finds applications in security surveillance, traffic monitoring, etc. In these applications, the movements of objects are constrained by structured environments. Therefore, the relationship between objects and environments can be exploited as additional information for improving the performance of tracking. We use the environment state to model the relationship between the objects and environments, and integrate it into the framework of Bayesian tracking. In this paper, distance transform is used to model the environment state, and particle filtering is employed as the paradigm for solving the Bayesian tracking problem. The adaptive dynamics model and environment prior are devised for the particle filter to fully utilize the environment information in the tracking process. Experiments on some video surveillance sequences demonstrate the effectiveness and robustness of our approach for tracking object motions in structured environments.