Robust and Fast Tracking Algorithm in Video Sequences by Adaptive Window Sizing Using a Novel Analysis on Spatiotemporal Gradient Powers

Abstract

Success of a tracking method depends largely on choosing the suitable window size as soon as the target size changes in image sequences. To achieve this goal, we propose a fast tracking algorithm based on adaptively adjusting tracking window. Firstly, tracking window is divided into four edge subwindows, and a background subwindow around it. Then, by calculating the spatiotemporal gradient power ratios of the target in each subwindow, four proper expansion vectors are associated with any tracking window sides such that the occupancy rate of the target in tracking window should be maintained within a specified range. In addition, since temporal changing of target is evaluated in calculating these vectors, we estimate overall target displacement by sum of expansion vectors. Experimental results using various real video sequences show that the proposed algorithm successfully track an unknown textured target in real time, and is robust to dynamic occlusions in complex noisy backgrounds.

Balloon energy based on parametric active contour and directional Walsh–Hadamard transform and its application in tracking of texture object in texture background

“…Various tracking methods have been proposed and improved, from the simple and rigid object tracking with static camera, to the complex and non-rigid object tracking with moving camera [5]. These methods are categorized into five groups [6,7] namely, region-based tracking [8], feature-based tracking [9], mesh-based tracking [10,11], model-based tracking [12], and active contour models (ACM)-based tracking [13].…”

Texture‐based parametric active contour for target detection and tracking

“…Then corresponding of each point is identified in the next frame and a motion vector is determined. Finally, the tracking window is set based on the motion vector (Verestoy and Chetverikov, 1998;Moallem et al, 2007).…”