Object tracking in an outdoor environment using fusion of features and cameras
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.
tracking extended kalman filter classification data fusion