Tracking people in crowds by a part matching approach

The major difficulty in human tracking is the problem raised by challenging occlusions where the target person is repeatedly and extensively occluded by either the background or another moving object. These types of occlusions may cause significant changes in the person's shape, appearance or motion, thus making the data association problem extremely difficult to solve. Unlike most of the existing methods for human tracking that handle occlusions by data association of the complete human body, in this paper we propose a method that tracks people under challenging spatial occlusions based on body part tracking. The human model we propose consists of five body parts with six degrees of freedom and each part is represented by a rich set of features. The tracking is solved using a layered data association approach, direct comparison between features (feature layer) and subsequently matching between parts of the same bodies (part layer) lead to a final decision for the global match (global layer). Experimental results have confirmed the effectiveness of the proposed method. © 2008 IEEE

Video tracking of people under severe occlusions

University of Technology, Sydney. Faculty of Engineering and Information Technology.Video surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision and pattern recognition. The goal of this research is to develop a real-time automatic tracking system which is both reliable and efficient by utilizing computational approaches. The literature has presented many valuable methods on object tracking; however, most of those algorithms can only perform effectively under simple scenarios. There are a few algorithms which attempt to accomplish object tracking in a complex dynamic scene and have successfully achieved their goals when the dynamic scene is not too complex. However no system yet is capable of accurately handling object tracking, especially human tracking, in a crowded environment with frequent and continuous occlusions. Therefore, the goal of my research is to develop an effective human tracking algorithm which takes into account and overcomes the various factors involved in a complex dynamic scene. The founding idea is that of dividing the human figure into five main parts, and track each individually under a constraint of integrity. Data association in new frames is performed on each part, and is inferred for the whole human figure through a fusion rule. This approach has proved a good trade off between model complexity and actual computability. Experimental results have confirmed the effectiveness of the methodology

A review of tracking methods under occlusions

Object tracking in computer vision refers to the task of tracking individual moving objects accurately from one frame to another in an image sequence. Several tracking methods have been proposed in the recent literature capable of coping with a certain degree of occlusions of the objects. However, no comparative analysis of such methods has been presented to date and both the expert and the newcomer to this area may be confused about the relative effectiveness of each method when compared under the same level of complexity of the dynamic scene. In order to fulfill this need, this paper proposes a set of analysis criteria and provides a comparative review of the main recent tracking methods, in particular with respect to their capability of tracking objects under occlusions