Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
An Adaptive Selection of the Scale and Orientation in Kernel Based Tracking
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
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A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- (GMM-) based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation. Unlike the standard GMM detection the proposed detection method considers spatial and temporal dependencies as well as a limitation of the standard deviation leading to a faster update of the mixture model and to smoo ther binary masks. The binary masks are transformed in such a way that the object size can be used for a simple but fast classification. The core of the tracking unit, named GMM-SAMT, is a shape adaptive mean shift- (SAMT-) based tracking technique, which uses Gaussian mixture models to adapt the kernel to the object shape. GMM-SAMT returns not only the precise object position but also the current shape of the object. Thus, Auto GMM-SAMT achieves good tracking results even if the object is performing out-of-plane rotations.