||Surveillance of large urban areas demands using 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 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 affect the way they are walking without it looking unnatural.
This project explores how gait can be used for reidentification. To determine this, a number of different gait--based methods used for identification of people were adopted for reidentification. The methods are based on the active energy image, gait energy image and four other methods. The methods are appearance based and the common theme is that a sequence of silhouettes of the subject is transformed into a 2D representation of the gait. This image is then used for reidentification by comparing it to other gaits in the pool using a simple classification method.
Two datasets were used to test the methods. The first dataset were captured with live surveillance cameras in an urban scene and the other 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 reidentification rate of 80%.