K-means Tracker: A General Algorithm for Tracking People

Abstract

In this paper, we present a clustering-based tracking algorithm for tracking people (e.g. hand, head, eyeball, body, and lips). It is always a challenging task to track people under complex environment, because such target often appears as a concave object or having apertures. In this case, many background areas are mixed into the tracking area which are difficult to be removed by modifying the shape of the search area during tracking. Our method becomes a robust tracking algorithm by applying the following four key ideas simultaneously: 1) Using a 5D feature vector to describe both the geometric feature “(x,y)” and color feature “(Y,U,V)” of each pixel uniformly. This description ensures our method to follow both the position and color changes simultaneously during tracking; 2) This algorithm realizes the robust tracking for objects with apertures by classifying the pixels, within the search area, into “target” and “background” with K-means clustering algorithm that uses both the “positive” and “negative” samples. 3) Using a variable ellipse model (a) to describe the shape of a nonrigid object (e.g. hand) approximately, (b) to restrict the search area, and (c) to model the surrounding non-target background. This guarantees the stable tracking of objects with various geometric transformations. 4) With both the “positive” and “negative” samples, our algorithm achieves the automatic self tracking failure detection and recovery. This ability makes our method distinctively more robust than the conventional tracking algorithms. Through extensive experiments in various environments and conditions, the effectiveness and the efficiency of the proposed algorithm is confirmed.