Traffic surveillance plays a vital role in computer vision and Intelligent Transportation Systems (ITS). Image analysis provides several effective techniques to detect moving objects in images. Thus, it has been extensively used for traffic monitoring systems. Recently, the problem of detecting and tracking vehicles is an important emerging research area for intelligent transportation systems. Many algorithms have been developed to detect and track moving vehicles either in daytime or in nighttime. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Building an integrated system to deal with daytime and nighttime is still a challenging problem especially when considering shadows at daytime, dim lighting at night, and real-time processing constraint.
In this paper, a vehicle tracking system is developed to deal with daytime and nighttime vehicles tracking. First, a daytime/nighttime detector is applied to the scene to determine the suitable technique. For daytime videos, shadows are removed from vehicles by applying a gamma decoding followed by a thresholding operation and employing an estimated background model of the video sequence. For nighttime videos, headlights and taillights are located and paired to initialize vehicles for tracking process. The experimental results have shown that the proposed method can effectively track vehicles in both daytime and nighttime. |
