Robust Object Tracking With Background-weighted Local Kernels

摘要

Object tracking is critical to visual surveillance,activity analysis and event/gesture recognition.The major issues to be addressed in visual tracking are illumination changes,occlusion,appearance and scale variations.In this paper,we propose a weighted fragment based approach that tackles partial occlusion.The weights are derived from the difference between the fragment and background colors.Further,a fast and yet stable model updation method is described.We also demonstrate how edge information can be merged into the mean shift framework without having to use a joint histogram.This is used for tracking objects of varying sizes.Ideas presented here are computationally simple enough to be executed in real-time and can be directly extended to a multiple object tracking system.