Robust Segmentation of Moving Vehicles Under Complex Outdoor Conditions

Abstract

Motion detection and segmentation of traffic vehicles in an outdoor environment, particularly under nonideal weather conditions, in the presence of camera noise and with variable or unfavorable luminance conditions is still an area of active research. Gaussian-based background modeling is commonly used to detect moving objects in computer vision systems. However, it has some limitations: It cannot effectively deal with sudden change in illumination, snowfall, fog, and repetitive motions such as swaying leaves. These nonideal outdoor conditions result in false motion detection. We propose an alternative technique to detect and segment the moving vehicles by making use of dynamically adaptive threshold using the full-search sum of absolute difference (FSSAD) algorithm. We show that motion energy that is obtained using sequence of frames can effectively be used to differentiate between moving vehicles and a dynamic background. Because FSSAD is computationally expensive, we propose a modification using the adaptive-motion threshold, which not only reduces the false motion but improves the computational efficiency as well. Performance evaluation of our proposed framework has been carried out on publicly available benchmark data sets. Qualitative comparison with other methods that are proposed in the literature shows that our approach achieves better segmentation and is suitable for real-time implementation.