An online learned hough forest model based on improved multi-feature fusion matching for multi-object tracking

Object tracking has been one of the most important and active research areas in the field of computer vision. In order to solve low accuracy in object occlusion and deformation for multi-object tracking, an online learned Hough forest model based on improved multi-feature fusion matching for multi-object tracking is proposed in this paper. Firstly, positive and negative samples are selected online according to low-level association among detection responses and construct the feature model of the object with color histogram, histogram of oriented gradient (HOG) and optical flow information. Secondly, longer trajectory associations are generated based on the online learned Hough forest framework. Finally, a trajectory matching algorithm based on multi-feature fusion is proposed, and we introduce two methods of similarity measure in color histogram and feature points matching based on the Gabor filter to generate the probability matrix with the weighted factor. Therefore, it can further form the complete trajectories of the objects by associating them gradually. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.