Digital video processing
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Color active shape models for tracking non-rigid objects
Pattern Recognition Letters - Special issue: Colour image processing and analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video object tracking with feedback of performance measures
IEEE Transactions on Circuits and Systems for Video Technology
Location-Aware multi-agent based intelligent services in home networks
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
A context-aware multi-agent service system for assistive home applications
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
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This paper presents a feature fusion-based tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed object tracking procedure can be divided into three steps: (i) localization of human objects, (ii) prediction and correction of the object’s location by utilizing spatio-temporal information, and (iii) restoration of occlusion using the NPT-AFM[15]. Feature points inside an ellipsoidal shape including objects are estimated instead of its shape boundary, and are updated as an element of the training set for the AFM. Although the proposed algorithm uses the greatly reduced number of feature points, the proposed feature fusion-based multiple people tracking algorithm enables the tracking of occluded people in complicated background.