Resolving occlusion ambiguity by combining Kalman tracking with feature tracking for image sequences
Mark Heimbach, Kamak Ebadi, Sally L. Wood · 2017
An improved method for mitigating occlusion in object tracking is proposed in this paper. Using more traditional methods of object tracking and feature detection, a novel scheme is developed based on the use of multiple tracking methods which operate in parallel to minimize the effect of occlusion. A popular feature detection algorithm called Histogram Oriented Gradients (HOG) is used as our baseline tracker. Its ability to detect and track objects during occlusion is then enhanced with a Kalman filter. State variables for Kalman include position and velocity of the object. Additionally, we introduce a new term called a Correlation Constant C(k) which makes use of a HOG trackers noise distribution to minimize the process and measurement variance of the Kalman filter. An online video database is used to experimentally verify our proposed algorithm [1]. Each video frame is provided with ground truth coordinates for the object being tracked. Results were developed in Matlab using online code developed by Henriques [2]. Experimental results show that our proposed algorithm is effective in solving the occlusion problem.