Frequency divergence image: A novel method for action recognition

Abstract

Action recognition systems have the potential to support clinicians, coaches and physical therapists in identifying important adopted movement patterns which could aid injury detection potential or inform rehabilitation strategies. Currently, motion capture systems, structured light pattern and time-of-flight sensors have utilization limitations that place constraints on their use outside of the laboratory setting. For this reason, we propose a system for human action recognition from video. The method presented in this work has utility with patient populations, such as Parkinson's disease, Alzheimer's disease, multiple sclerosis and dementia, outside of laboratory setting to detect the degree of which, and progression of, gait pathology. We developed a novel vision algorithm for template matching—the characterization of the motion in a video sequence. The method, titled Frequency Divergence Image, is a paradigm shift in template matching methods. Template matching methods measure macro-motion, whereas the proposed method detects micro-motion that differs from the flow of the action. We show that micro-cues improve prediction performance of human action on a real-world data set. We demonstrate a 9.15% improvement in classification accuracy over the original Motion History Image formulation when used with a convolutional neural network. Future work will focus on the deployment of the system to identify gait pathology from various patient populations.