Continuous activity understanding based on accumulative pose-context visual patterns

In application domains, such as human-robot interaction and ambient intelligence, it is expected that an intelligent agent can respond to the person's actions efficiently or make predictions while the person's activity is still ongoing. In this paper, we investigate the problem of continuous activity understanding, based on a visual pattern extraction mechanism which fuses decomposed body pose features from estimated 2D skeletons (based on deep learning skeleton inference) and localized appearance-motion features around spatiotemporal interest points (STIPs). Considering that human activities are observed and inferred gradually, we partition the video into snippets, extract the visual pattern accumulatively and infer the activities in an online fashion. We evaluated the proposed method on two benchmark datasets and achieved 92.6% on the KTH dataset and 92.7% on the Rochester Assisted Daily Living dataset in the equilibrated inference states. In parallel, we discover that context information mainly contributed by STIPs is probably more favourable to activity recognition than the pose information, especially in scenarios of daily living activities. In addition, incorporating the visual patterns of activities from early stages to train the classifier can improve the performance of early recognition; however, it could degrade the recognition rate in later time. To overcome this issue, we propose a mixture model, where the classifier trained with early visual patterns are used in early stages while the classifier trained without early patterns are used in later stages. The experimental results show that this straightforward approach can improve early recognition while retaining the recognition correctness of later times.