Front vehicle tracking using scene analysis

This paper presents a design and implementation of a real-time visual tracking system for vehicle safety applications. A novel feature-based vehicle tracking algorithm is proposed. This algorithm can automatically detect and track multiple moving objects, including cars and motorcycles, ahead of the tracking vehicle. Combined with the concept of focus of expansion (FOE) and scene analysis, the developed system can segment features of moving objects from moving background and provide a collision warning in real time. The proposed algorithm is realized using a CMOS image sensor and Nios embedded processor architecture. The constructed stand-alone visual tracking system has been validated in actual road tests. Experimental results show that the proposed system successfully tracks front vehicles and provides information of collision warning in urban artery with speed around 60 km/hr both at night and day times.