COMPUTATIONALLY EFFICIENT RECOGNITION OF ACTIVITIES OF DAILY LIVING

In this work, we propose a computationally efficient method for the recognition of human activities of daily living. Our method uses trajectories of tracked visual features extracted on dense grids and performs recognition via Support Vector Machines (SVMs). In contrast to State-of-the-Art approaches, which are based on dense optical flow (OF), we use fast block matching motion estimation, resulting in increased computational efficiency, with minimal loss in terms of recognition accuracy. To prove the effectiveness of our approach, we have conducted experiments on benchmark datasets of videos of human activities of daily living, demonstrating the trade-offs between recognition accuracy and computational efficiency.