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In this research, we proposed testing and validating the accuracy of employing wavelet transform and Hidden Conditional Random Field (HRCF) for video based activity recognition. For feature extraction, Symlet wavelet was tested and decomposed up to 4 levels, and some of the highest coefficients were extracted from each level of decomposition. These coefficients were based on the average frequency of each video frame and the time difference between each frame. Finally, a novel HRCF model was applied for recognition. The proposed method was tested on a database of ten activities, where the data were collected from nine different people, and compared with one of the existing techniques. The overall recognition rate, using the symlet wavelet family (Symlet 4), was 93% that showed an improvement of 13% in performance.

VIDEO BASED HUMAN ACTIVITY RECOGNITION USING WAVELET TRANSFORM AND HIDDEN CONDITIONAL RANDOM FIELDS (HCRF)