Summary: Object tracking in an outdoor environment using
fusion of features and cameras
*, J.K. Aggarwalb
Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Received 7 April 2003; received in revised form 6 June 2005; accepted 7 June 2005
This paper presents methods for tracking moving objects in an outdoor environment. A robust tracking is achieved using feature fusion and
multiple cameras. The proposed method integrates spatial position, shape and color information to track object blobs. The trajectories
obtained from individual cameras are incorporated by an extended Kalman filter (EKF) to resolve object occlusion. Our results show that
integrating simple features makes the tracking effective and that EKF improves the tracking accuracy when long-term or temporary occlusion
occurs. The tracked objects are successfully classified into three categories: single person, people group, or vehicle.
q 2005 Elsevier B.V. All rights reserved.
Keywords: Tracking; Classification; Extended Kalman filter; Data fusion
The efficient tracking and classification of multiple
moving objects is a challenging and important task in