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A robust tracking algorithm based on the particle filter with multi-cue adaptive fusion is proposed which can overcome the shortcoming of single visual cue in complex environments. The color and the texture based on the discrete wavelet transform (DWT) are used to describe the tracking target. The weights are adaptively adjusted using the democratic integration according to the current tracking situations,...
In this paper, we propose a robust vehicle tracker for Infrared (IR) videos motivated by the recent advance in compressive sensing (CS). The new eL1-PF tracker solves a sparse model representation of moving targets via L1 regularized least squares. The sparse-model solution addresses real-world environmental challenges such as image noises and partial occlusions. To further improve tracking performance...
Most surveillance systems adopt the paradigm that first detect and then track. However, it is hard to determine the most suitable threshold for detection since the surveillance view is unpredictable for an airborne platform. In contrast to the mainstream approach, we propose a novel approach allowing simultaneous detection and tracking. In the proposed scheme, the detection and tracking are not independent...
A method based on the particle filter was proposed to resolve the problem of target track in looking forward sonar image sequences. The theory of particle filter was illustrated, and it was used to estimate the situation of objects in sonar images. Considering the relation between the process noise and the steady results, process noise was set by the adaptive strategy. At last, the results of experiments...
A novel target tracking algorithm for forward-looking infrared image sequences is proposed based on mean shift and particle filter algorithm. The mean shift algorithm is served as an efficient gradient estimation and mode seeking procedure in the particle filter. Particles move toward the modes of the posterior kernel density estimation. The infrared target is represented in the cascade grey space...
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