Abstract—Abstract: The view scope of a single camera is finite and limited by scene structures. Multi-fisheye cameras can monitor a wide area and trace a complete trajectory of a moving object. In this study, an automatic detection and tracking system with two fisheye cameras for environment surveillance is proposed. The proposed system is composed of two major modules: foreground detection and foreground tracking. The background subtraction method is first applied to extract targets. Then use Kalman filtering for pedestrian motion prediction. A transform table is pre-established to associate multi-cameras data in the overlapping areas. When object across disjoint camera views, the data in the lookup table can provide enough information to realize the moving object in camera views actually belonging to the same object, and keep consistent labels on the object. To improve the reliability of the tracking performance, motion and color appearance features are used to match the detected objects in different cameras. It demonstrates that the proposed method can work well under challenging conditions, such as light change, shadow interference, object occlusion.

Index Terms—Key words: Surveillance system, multi-fisheye cameras, Kalman filter, codebook background model.

The Institute of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan.

Cite:Dinchang Tseng, Chienhung Chen, Chiahui Tseng, "Automatic Detection and Tracking in Multi-fisheye Cameras Surveillance System," International Journal of Computer and Electrical Engineering vol. 9, no. 1, pp. 370-383, 2017.