Low complexity-low power object tracking using dynamic quadtree pixelation and macroblock resizing

Abstract

In this paper, a high speed, reliable, low memory demanding and precise object detection and tracking algorithm is proposed. The proposed work uses a macroblock of rectangular shape, which is placed in the very first frame of the video to detect and track a single moving object using monocular camera. The macroblocks are positioned in the field of view (FOV) of camera where the probability of occurrence of object is high. After placing macroblocks, a threshold value is examined to detect the presence of objects in the selected macroblocks. Afterwards, a quadtree approach is used to minimize the bounding box and to reduce the pixelation. A tracking algorithm is proposed which illustrates a unique method to find the moving directional vectors. The proposed method is based on macroblock resizing, which demonstrates an accuracy rate of 98.5% with low memory utilization.