Using mel-frequency audio features from footstep sound and spatial segmentation techniques to improve frame-based moving object detection
Moving object detection in video streams is a challenging and integral part of computer vision which is used in surveillance, traffic and site monitoring, and navigation. Compared with the background-based techniques, frame differencing technique is computationally inexpensive. However, frame differencing technique only detects the boundary of a moving object. Due to changing light conditions, shadows, poor contrast between object and background, and a slow-moving object, object detection rate from frame differencing technique reduces. This is because the number of noisy frames and frames with missing/partially detected object increases. Application of large kernel size morphological operations fails to remove noise as they might remove the boundary (or part) of a moving object. In this study, the authors propose a methodology to improve the frame differencing technique using footstep sound generated by a moving object. Audio recorded with the video system is processed and footstep sound is detected using audio features computed as mel-frequency cepstral coefficients. Number of frames within each footstep sound are counted and processed. Spatial segmentation is used to find the moving object in noisy frames. A missing or partially detected object is recovered by modelling an ellipse using a moving object from other neighbourhood frames.