Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Tracking people across disjoint camera views by an illumination-tolerant appearance representation
Machine Vision and Applications
Computer Vision and Image Understanding
Object matching in disjoint cameras using a color transfer approach
Machine Vision and Applications
A framework for track matching across disjoint cameras using robust shape and appearance features
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Continuously tracking objects across multiple widely separated cameras
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Hi-index | 0.00 |
Human matching between different fields of view is a difficult problem in intelligent video surveillance; whereas fusing multiple features has become a strong tool to solve it. In order to guide the fusion scheme, it is necessary to evaluate the matching performance of these features. In this paper, four typical features are chosen for the evaluation. They are the Color Histogram, UV Chromaticity, Major Color Spectrum Histogram, and Scale-Invariant Features (SIFT). Quantities of video data are collected to test their general accuracy, robustness, and real-time applicability. The robustness is measured under the conditions of illumination changes, Gaussian and salt noises, foreground errors, resolution changes, and camera angle differences. The experimental results show that the four features bear distinctive performances under the different conditions, which will provide important references for the feature fusion methods.