Detecting and Tracking Moving Objects in Sequences of Color Images

Abstract

A statistical change detector, implemented as a zero-latency finitememory filter, is used to identify anomalies in temporal pixel statistics. An F-distributed test statistic is computed for each pixel and used in a hypothesis test. The tracker, with automatic track initiation and termination, uses a low-complexity pairwise Joint Probabilistic Data Association (JPDA) algorithm, which has been restricted to consider clusters (sub-problems) containing no more than two tracks. The track state and clutter model are augmented to include color. The detector and tracker are used to process sample video data.

Video categorization using object of interest detection

“…Detecting the OOI in a series of images is already a mature technique in surveillance and object segmentation [6]. Recently, this method was extended from a group of images to videos, for tasks like video segmentation, object tracking, video retrieval and key frame selection [8][9][10].…”