Human action recognition using hierarchic body related occupancy maps

This paper introduces a novel spatial method for human action recognition that is discriminative without needing temporal information or action key poses. First, skeletal data is acquired with the Microsoft Kinect v2 sensor and undergoes a Pose Invariant Normalization (PIN) process. The PIN process translates, rotates and scales the various observed poses to eliminate body differences and positional differences between subjects. Second, the method uses a Body Related Occupancy Map (BROM), that describes in a 3D grid how the area around specific body parts is used, as a strong indicator of the particular action that is being performed. The BROM and its 2D projections are used as feature inputs for Random Forest classifiers. These classifiers are then combined in a hierarchic structure to boost the classification performance. The approach is tested on a self-captured database of 23 human actions for game-play. On this database a classification with an accuracy score of 91% is achieved for the hierarchic BROM (HiBROM) classification. On the public CAD60 dataset, the HiBROM classifier attains 87.2% accuracy which is comparable to other state-of-the-art methods.