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Unformatted text preview: Detecting Pedestrians Using Patterns of Motion and Appearance Paul Viola Michael J. Jones Daniel Snow Microsoft Research Mitsubishi Electric Research Labs Mitsubishi Electric Research Labs firstname.lastname@example.org email@example.com firstname.lastname@example.org Abstract This paper describes a pedestrian detection system that in- tegrates image intensity information with motion informa- tion. We use a detection style algorithm that scans a detec- tor over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walk- ing person. Past approaches have built detectors based on motion information or detectors based on appearance in- formation, but ours is the first to combine both sources of information in a single detector. The implementation de- scribed runs at about 4 frames/second, detects pedestrians at very small scales (as small as 20x15 pixels), and has a very low false positive rate. Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: i) devel- opment of a representation of image motion which is ex- tremely efficient, and ii) implementation of a state of the art pedestrian detection system which operates on low res- olution images under difficult conditions (such as rain and snow). 1 Introduction Pattern recognition approaches have achieved measurable success in the domain of visual detection. Examples include face, automobile, and pedestrian detection [14, 11, 13, 1, 9]. Each of these approaches use machine learning to construct a detector from a large number of training examples. The detector is then scanned over the entire input image in order to find a pattern of intensities which is consistent with the target object. Experiments show that these systems work very well for the detection of faces, but less well for pedes- trians, perhaps because the images of pedestrians are more varied (due to changes in body pose and clothing). Detec- tion of pedestrians is made even more difficult in surveil- lance applications, where the resolution of the images is very low (e.g. there may only be 100-200 pixels on the target). Though improvement of pedestrian detection using better functions of image intensity is a valuable pursuit, we take a different approach. This paper describes a pedestrian detection system that integrates intensity information with motion information. The pattern of human motion is well known to be readily distinguishable from other sorts of motion. Many recent papers have used motion to recognize people and in some cases to detect them ([8, 10, 7, 3]). These approaches have a much different flavor from the face/pedestrian detection approaches mentioned above. They typically try to track moving objects over many frames and then analyze the mo- tion to look for periodicity or other cues....
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This note was uploaded on 06/13/2011 for the course CAP 6412 taught by Professor Staff during the Spring '08 term at University of Central Florida.
- Spring '08