Robust Video Object Segmentation Algorithm for Complex Outdoor Conditions

In this paper, detecting and segment the moving vehicles by making use of dynamically adaptive threshold using the full-search sum of absolute difference (FSSAD) algorithm. In the past, Gaussian based background model is used to detect the moving objects. It cannot effectively deal with sudden change in illumination, snowfall, fog, and repetitive motions such as swaying leaves. In this paper, FFSAD follows three steps. First step is, calculate the motion energy using differentiate between moving vehicles and a dynamic background. Next step is modification using the adaptive-motion threshold, which not only reduces the false motion but improves the computational efficiency as well. Final step is the boundary extraction of moving vehicles. The experimental results show that the comparison with other methods that are proposed in the literature shows that our approach achieves better segmentation and is suitable for real-time implementation. Index term: Block-matching algorithm (BMA), challenging weather conditions, traffic monitoring, vehicle segmentation.