Detection of vehicle in pedestrian pathway using defined range approach

Detection of vehicles in a pedestrian pathway is important for the safety of pedestrians. Existing methods examine the entire frame for feature extraction and detect vehicles. However in real-time surveillance, analyzing the entire frame is computationally complex. To reduce the computational complexity, this paper proposes an efficient vehicle detection scheme. Using defined range approach, the background is subtracted and foreground blobs obtained from the background subtraction, the probability of the blobs in which the presence of the vehicles is expected is identified by a statistically computed predefined area criterian. These identified blobs within the defined range are taken as the Region of Interest (ROI). The Haar features are extracted from these ROI and then provided to Cascaded Classifier, which is trained apriori to detect vehicles. The proposed scheme is implemented using OpenCV, tested in real-time and found to give faster and better detection rate compared to existing method.