Background subtraction is widely used for the detection of objects moving against a background in video. Moving objects are detected by comparing the current image with the extracted background image (BI). The BI is constructed using a single or several frames without moving foreground object in the scene. The pixel in BI is updated by the current pixel value if this pixel is determined to be part of the background during the subtraction process. The drawback of this approach is the requirement of the establishment of a background image prior to the detection; otherwise any object that appears in the first frame is detected as “moving object” through the whole sequence. An even more serious problem occurs when there is a sudden change in the background, such as a light being turned on or off, or a newly arrived “still” object. As long as the pixel value change is larger than the threshold, the “still” object after the sudden change will not be included in the background image and hence it will appear as a moving object in the following frames. To avoid these problems, we propose an approach in which a second updated background image, BI2, is stored. BI2 is initially constructed from the first frame detected and then updated through the detection processes with criteria different to that used in updating BI. The pixels in BI2 are updated if they have been determined as background pixels by comparing the difference between the current and previous frames. By using this method, the “still” objects are not falsely detected as moving objects. After a few frames, the “still” objects are updated into the background image BI2. BI2 is then incorporated into the background image BI. Moving objects are then subtracted from the modified background image BI and the “still” objects are eliminated.
In this paper, we present a multisensor surveillance system that consists of an optical sensor and an infrared sensor. In this system, a background subtraction method based on the zero-order statistics is presented for the moving object segmentation. Additionally, we propose an iterative method for multisensor video registration based on the robust Hausdorff distance. An efficient face detection system is shown as an application that will have enhanced performance based on the registration and fusion of the information from the two sensors. Experimental results show the efficacy of the proposed system.