Object Tracking with Target and Background Samples

• HUA Chunsheng

  The authors are with the Faculty of System Engineering, Wakayama University

• WU Haiyuan

  The authors are with the Faculty of System Engineering, Wakayama University

• CHEN Qian

  The authors are with the Faculty of System Engineering, Wakayama University

• WADA Toshikazu

  The authors are with the Faculty of System Engineering, Wakayama University

Abstract

In this paper, we present a general object tracking method based on a newly proposed pixel-wise clustering algorithm. To track an object in a cluttered environment is a challenging issue because a target object may be in concave shape or have apertures (e.g. a hand or a comb). In those cases, it is difficult to separate the target from the background completely by simply modifying the shape of the search area. Our algorithm solves the problem by 1) describing the target object by a set of pixels; 2) using a K-means based algorithm to detect all target pixels. To realize stable and reliable detection of target pixels, we firstly use a 5D feature vector to describe both the color ("Y,U,V") and the position ("x,y") of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking. Secondly, we use a variable ellipse model to describe the shape of the search area and to model the surrounding background. This guarantees the stable object tracking under various geometric transformations. The robust tracking is realized by classifying the pixels within the search area into "target" and "background" groups with a K-means clustering based algorithm that uses the "positive" and "negative" samples. We also propose a method that can detect the tracking failure and recover from it during tracking by making use of both the "positive" and "negative" samples. This feature makes our method become a more reliable tracking algorithm because it can discover the target once-again when the target has become lost. Through the extensive experiments under various environments and conditions, the effectiveness and efficiency of the proposed algorithm is confirmed.

Journal

• IEICE Transactions on Information and Systems, D

  IEICE Transactions on Information and Systems, D 90 (4), 766-774, 2007-04-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• object tracking
• K-means clustering
• tracking failure detection and recovery
• background interfusion

Details

• CRID: 1570009752602236160
• NII Article ID: 110007519538
• NII Book ID: AA10826272
• ISSN: 09168532
• Text Lang: en
• Data Source:
  • CiNii Articles