In most video surveillance applications, a fundamental activity is the detection of objects of interest, the tracking of those objects through the video sequence, and the determination of inferences from the object's activities. Nearly all commercially available surveillance algorithms that are presently available depend upon motion detection to isolate objects of interest. However, motion detection has its limits, such as the difficulty in distinguishing between different types of moving objects (e.g., cars, people, and trees) and the difficulty in distinguishing between closely aligned objects (e.g., two people walking next to each other).
To address these problems, attempts have been made to develop techniques that are specifically designed to detect people in video data. However, even when an algorithm is specifically designed for the detection of people, difficulties persist such as large variations in global and local appearances due to changes in clothing styles and camera angles, the effect of extraneous matter such as backpacks, and the occlusion of parts of a person in a scene by other people or objects.