Eigenspace-based fall detection and activity recognition from motion templates and machine learning

Automatic recognition of anomalous human activities and falls in an indoor setting from video sequences could be an enabling technology for low-cost, home-based health care systems. Detection systems based upon intelligent computer vision software can greatly reduce the costs and inconveniences associated with sensor based systems. In this paper, we propose such a software based upon a spatio-temporal motion representation, called Motion Vector Flow Instance (MVFI) templates, that capture relevant velocity information by extracting the dense optical flow from video sequences of human actions. Automatic recognition is achieved by first projecting each human action video sequence, consisting of approximately 100 images, into a canonical eigenspace, and then performing supervised learning to train multiple actions from a large video database. We show that our representation together with a canonical transformation with PCA and LDA of image sequences provides excellent action discrimination. We also demonstrate that by including both the magnitude and direction of the velocity into the MVFI, sequences with abrupt velocities, such as falls, can be distinguished from other daily human action with both high accuracy and computational efficiency. As an added benefit, we demonstrate that, once trained, our method for detecting falls is robust and we can attain real-time performance.