Tracking with probabilistic background model by density forests
Daimu Oiwa, Shinji Fukui, Yuji Iwahori, Tsuyoshi Nakamura, Manas Kamal Bhuyan · 2016
This paper proposes an approach for a tracking method robust to the intersection with objects with appearances similar to a target object. The proposed method targets image sequences taken by a moving camera and is based on the particle filter. Tracking methods using color information tend to track mistakenly a background region or an object with color similar to the target object. The method constructs the probabilistic background model by the histogram of the optical flow and defines the likelihood function so that the likelihood in the region of the target object may become large. This causes increasing the accuracy of tracking. The probabilistic background model is made by the density forests. It can infer a probabilistic density fast. Results are demonstrated by experiments using the real videos of outdoor scenes.