A refined object detection method based on HTM
Object detection plays a fundamental role in many content-based video systems. Often, it is still challenging to achieve both a reasonable accuracy and a fairly fast processing speed. In this paper, we propose a new object detection framework which utilizes raw RGB data from the pixel domain and some useful coding information from the compressed domain jointly. Firstly, various pixel-level detection algorithms can be embedded in our framework so as to obtain the preliminary results. Then by segmenting the moving regions from the background with the Hit-times Map (HTM), some false results can be removed and meanwhile the detection process can also be accelerated since the search area for sliding the detection window has been restricted to relatively small regions. After that, an additional regulation process is performed to further refine the preliminary detection results by employing both temporal consistency and spatial compactness in the motion vector(MV) field. The experimental results on two benchmark datasets show that the proposed method achieves a remarkable improvement both in detection accuracy and processing speed.