Abstract
Human activity recognition is one of the important and difficult problems in computer vision and machine learning applications. Automated human activity recognition system based on dense flow trajectory and hidden Markov model (HMM) is proposed. A 3D dense trajectory was formed by tracking the scale-invariant points from frame to frame. Maximum of 100 points per frame were considered for tracking. Histogram of gradient and dense trajectory descriptor features were extracted from cleaned trajectory and used for training hidden Markov models for each activity. Analysis of variance test resulted in F value of 1150.89 and 74.29 for histogram of gradient (HOG) and dense trajectory descriptor, respectively, for Weizmann database and 187.08 for combined features for videos recorded at Indian institute of technology Patna. Maximum accuracy of 100% is achieved for Weizmann database and IIT Patna database using hierarchical HMM.
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Dash, D.P., Kolekar, M.H. (2018). Dense Optical Flow Trajectory-Based Human Activity Recognition Using Hierarchical Hidden Markov Model. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_9
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