Gait-based Reidentification of People in Urban Surveillance Video
Daniel A. Skog · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2010
Video surveillance of large urban areas demands the use of multiple cameras. consider tracking a person moving between cameras in such a system. When the person disappears from the view of one camera and then reappears in another, the surveillance system should be able to determine that the person has been seen before and continue tracking. The process of determining this connection is known as reidentification. Gait is a biometric that has been shown to be useful in determining the identities of people. It is also useful for reidentification as it is not affected by varying lighting conditions between cameras. Also, it is hard for people to alter the way they are walking without it looking unnatural. This project explores how gait can be used for reidentification. To investigate this, a number of different gait--based methods used for identification of people were used for reidentification. The methods are based on the active energy image, gait energy image, frame difference energy image, contours of silhouettes, and the self--similarity plot. The Fourier transform of the gait silhouette volume will also be tested. These methods are appearance based and the common theme is that a sequence of silhouettes of the subject is transformed into a representation of the gait. The representations are then used for reidentification by comparing them to other gaits in a pool using a simple classification method based on the nearest neighbor classifier. Two datasets were used to test the methods. The first dataset was captured with live surveillance cameras in an urban scene and the second using a home video camera. The lower quality of the footage in the first dataset affected the results, obtaining only about 34% correct reidentifications. This can be compared with the higher quality dataset which gave a result of about 80% correct reidentifications.