Multi-feature Graph-Based Object Tracking

We present an object detection and tracking algorithm that addresses the problem of multiple simultaneous targets tracking in realworld surveillance scenarios. The algorithm is based on color change detection and multi-feature graph matching. The change detector uses statistical information from each color channel to discriminate between foreground and background. Changes of global illumination, dark scenes, and cast shadows are dealt with a pre-processing and post-processing stage. Graph theory is used to find the best object paths across multiple frames using a set of weighted object features, namely color, position, direction and size. The effectiveness of the proposed algorithm and the improvements in accuracy and precision introduced by the use of multiple features are evaluated on the VACE dataset.

• Type: Conference
• Year: 2006
• Where: CLEAR
• Authors: Murtaza Taj, Emilio Maggio, Andrea Cavallaro
• Keywords: Algorithm, Biometrics, CLEAR 2006, Color Change Detection, Multiple Simultaneous Targets