An Object Reconstruction Algorithm for Moving Vehicle Detection Based on Three-Frame Differencing
Yao Lin, Ma Fang, Shihong Duan · 2015
There are some defects in traditional three-frame differencing algorithm for moving vehicle detection, such as intermittent moving region, low detection rate of dark moving objects, multi-target cross and target splitting. This paper proposes an object reconstruction algorithm for moving vehicle detection based on three-frame differencing (ORTFD). The linear point operation on the original video image is employed to reduce the dispersion of dark moving vehicles. Three-frame differencing operation is used to get the contours of moving vehicles, and perform morphological processing on the binary image is adopted to exclude non target objects, also to improve the integrity of the obtained contours. Finally, progressive scan on the whole binary image is taken to find out connected region surrounded by the contour, a preset-threshold-based region filling method (PTRF) is designed to reconstruct the moving object. Algorithm simulation results show that ORTFD improve the detection rate of dark moving vehicle object, and accelerate the detection speed, the precision ratio of moving vehicle detection has been also improved. ORTFD algorithm can detect and recognize vehicles in real-time with high-accuracy for multiple targets under complex traffic conditions.