Human Activity Recognition using depth body part histograms and Hidden Markov Models
This paper proposes a novel approach for human activity recognition based on body part histograms and Hidden Markov Models. From a depth video frame, body parts are segmented first using a trained random forest. Then, a histogram for each body part is combined to represent histogram features for a depth image. The depth video activity features are then applied on hidden Markov models for training and recognition. The proposed method was superior when compared with other conventional approaches.
2016 International Conference on Innovations in Science, Engineering and Technology (ICISET)