Tracking and data association
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A General Framework for Temporal Video Scene Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Activity recognition consists of two fundamental tasks: tracking the features/objects of interest, and recognizing the activities. In this paper, we show that these two tasks can be integrated within the framework of a dynamical feedback system. In our proposed method, the recognized activity is continuously adapted based on the output of the tracking algorithm, which in turn is driven by the identity of the recognized activity. A non-linear, non-stationary stochastic dynamical model on the “shape” of the objects participating in the activities is used to represent their motion, and forms the basis of the tracking algorithm. The tracked observations are used to recognize the activities by comparing against a prior database. Measures designed to evaluate the performance of the tracking algorithm serve as a feedback signal. The method is able to automatically detect changes and switch between activities happening one after another, which is akin to segmenting a long sequence into homogeneous parts. The entire process of tracking, recognition, change detection and model switching happens recursively as new video frames become available. We demonstrate the effectiveness of the method on real-life video and analyze its performance based on such metrics as detection delay and false alarm.