Neural approach for object tracking in complex environment
Abstract
In this article, we present an algorithm to track objects in complex environments like, large variations in scale and orientation, background clutters, illumination changes, pose variation and occlusion. A multilayer perceptron based discriminative appearance model is constructed to distinguish the objects from their cluttered backgrounds. Moments of the binary image are used to estimate scale and orientation of the detected object. The target in the current frame is tracked by maximizing the Bhattacharyya coefficient between the distributions of object in the target and target candidate models. Two different heuristics based on support value and relative confidence score calculated from detection result are used to reduce drift problem and to handle occlusion. We show a realization of the proposed method and demonstrate its performance with respect to state-of-the-art techniques on several challenging video sequences. Analysis of the results concludes that the proposed method can track objects in a better way compare to the existing ones.
Authors: Ajoy Mondal, Ashish Kumar Ghosh, Susmita Ghosh