Vehicle Detection , Classification and Position Estimation based on Monocular Video Data during Night-time

1 Abstract This study describes an effective method for detecting, tracking and classifying vehicles during night-time in order to support automotive adaptive illumination applications. The hereby described software framework, which computes the relative position, velocity and estimated class of all detected vehicles, integrates multiple processing stages. Firstly, an image segmentation using a threshold method to detect all light sources in the image. Secondly, possible pairs of headand taillight are clustered using geometrical information. Thirdly, all detected objects are tracked using a Kalman-Filter to increase resolution and robustness of the algorithm. Lastly, a method for computing distance and velocity for all classified objects (e.g. cars, trucks, bikes...) is presented. The system is tested to run in real-time and some results and conclusions are offered at the end.

[1]  Christoph Stiller,et al.  A hardware and software framework for automotive intelligent lighting , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[2]  Ming-Yang Chern,et al.  The lane recognition and vehicle detection at night for a camera-assisted car on highway , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[3]  Chun-Jen Chen,et al.  A linear-time component-labeling algorithm using contour tracing technique , 2004, Comput. Vis. Image Underst..

[4]  Tieniu Tan,et al.  A real-time object detecting and tracking system for outdoor night surveillance , 2008, Pattern Recognit..

[5]  Amnon Shashua,et al.  Vision-based ACC with a single camera: bounds on range and range rate accuracy , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[6]  Bing-Fei Wu,et al.  Nighttime Vehicle Detection for Driver Assistance and Autonomous Vehicles , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[7]  M. Goebl,et al.  A Real-Time-capable Hard-and Software Architecture for Joint Image and Knowledge Processing in Cognitive Automobiles , 2007, 2007 IEEE Intelligent Vehicles Symposium.