Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Using Histograms to Detect and Track Objects in Color Video
AIPR '01 Proceedings of the 30th on Applied Imagery Pattern Recognition Workshop
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust detection of moving objects in video sequences through rough set theory framework
Image and Vision Computing
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A 24/7 traffic surveillance system needs to perform robustly in dynamic traffic conditions. Despite the amount of work that has been done in creating suitable background models, we observe limitations with the state-of-the-art methods when there is minimal color information and the background processes have a high variance due to lighting changes or adverse weather conditions. To improve the performance, we propose in this paper a Difference of Gaussian (DoG) edge-texture based modeling for learning the background and detecting vehicles in such conditions. Background DoG images are obtained at different scales and summed to obtain an Added DoG image. The edge-texture information contained in the Added DoG image is modeled using the Local Binary Pattern (LBP) texture measure. For each pixel in the Added DoG image, a group of weighted adaptive LBP histograms are obtained. Foreground vehicles are detected by matching an existing histogram obtained from the current Added DoG image to the background histograms. The novelty of this technique is that it provides a higher level of learning by establishing a relationship an edge pixel has with its neighboring edge and non-edge pixels which in turn provides us with better performance in foreground detection and classification.