基于自适应时空码书的粒子滤波多目标跟踪

提出一种基于自适应时空码书检测模型的粒子滤波多目标跟踪算法。使用时空码书模型进行前景背景分割，检测出前景目标，在该模型上加入目标自适应过程。将自适应时空码书检测的结果作为粒子滤波跟踪算法的初始目标状态，通过关联算法和粒子滤波实现多目标跟踪。自适应时空码书模型能明显降低对前景目标的误检率，抑制噪声干扰。实验结果表明，该算法能够在有干扰的复杂背景下实现对运动多目标的快速捕获，并有效提高跟踪的可靠性和精度。

Particle Filtering Multi-objective Tracking Based on Adaptive Spatio-temporal Codebook

A particle filtering tracking algorithm based on adaptive spatio-temporal Codebook detection model is designed to realize multi-objective tracking. Spatio-temporal codebook model is used for foreground-background segmentation as well as detecting foreground targets, and an adaptive phrase of the targets is added to this model. The prior target state distribution of particle filter is generated by detected foreground targets, using association algorithm and particle filter to realize multi-objective tracking. Compared with traditional codebook and spatio-temporal codebook model, the proposed adaptive spatio-temporal codebook model has robustness to interference and noise. Experimental results show that the particle filter tracking algorithm can capture moving targets rapidly with higher validity in moving background with illumination change in existence of interference and noise.