Human Carrying Status in Visual Surveillance

IEEE Computer Society
Publication Type:
Conference Proceeding
Proceedings 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, 2 pp. 1670 - 1677
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011001838OK.pdf801.38 kB
Adobe PDF
A personâs gait changes when he or she is carrying an object such as a bag, suitcase or rucksack. As a result, human identification and tracking are made more difficult because the averaged gait image is too simple to represent the carrying status. Therefore, in this paper we first introduce a set of Gabor based human gait appearance models, because Gabor functions are similar to the receptive field profiles in the mammalian cortical simple cells. The very high dimensionality of the feature space makes training difficult. In order to solve this problem we propose a general tensor discriminant analysis (GTDA), which seamlessly incorporates the object (Gabor based human gait appearance model) structure information as a natural constraint. GTDA differs from the previous tensor based discriminant analysis methods in that the training converges. Existing methods fail to converge in the training stage. This makes them unsuitable for practical tasks. Experiments are carried out on the USF baseline data set to recognize a humanâs ID from the gait silhouette. The proposed Gabor gait incorporated with GTDA is demonstrated to significantly outperform the existing appearance-based methods.
Please use this identifier to cite or link to this item: