8 November 1999 Trajectory guided recognition of actions
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A computer vision method is presented for recognizing the non-rigid motion observed in objects moving in a 3D environment. This method is embedded in a more complete mechanism that integrates low-level (image processing), mid- level (recursive 3D trajectory estimation), and high-level (action recognition) processes. Multiple moving objects are observed via a single, uncalibrated video camera. A Kalman filter formulation is used in estimating the relative 3D motion trajectories. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages. In this paper we concentrate in the action recognition stage. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is then proposed as an efficient method for adaptive classification of action. The TGR approach is demonstrated using 'motion history images' that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Romer Rosales, Stan Sclaroff, "Trajectory guided recognition of actions", Proc. SPIE 3840, Telemanipulator and Telepresence Technologies VI, (8 November 1999); doi: 10.1117/12.369285; https://doi.org/10.1117/12.369285

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