Multi-object tracking algorithm based on adaptive mixed filtering

According to the main problems of multi-object video tracking such as objects collision, merging and splitting, a novel multi-object tracking algorithm based on adaptive mixed filtering is proposed. An adaptive background mixture Gaussian model is adopted to obtain the foreground image, and a simple shadow elimination algorithm is also presented, which describes the HSV components with unified weighted forms, and dose not need judge each component one by one, when it judges the pixels of foreground image. When measured values are extracted from the foreground image, a merging algorithm is introduced, which merges divided detection rectangles into one. Then, the detected foreground measured values are associated with the existing objects based on reasoning methods, and the multiple objects are tracked with adaptive mixed filtering. The algorithm combines the mean shift algorithim which meets the demand of real-time request with the particle filtering one with high reliability when objects are blocked. Simulation experiment proves that the algorithm can track multiple objects efficiently, judge appearance and disappearance of objects accurately, and solve the problems of multi-object blockage, merging and splitting.