Efficient Human Vision Inspired Action Recognition using Adaptive Spatiotemporal Sampling
This work addresses the challenge of always-on action recognition for wearable devices, offering an incremental improvement in efficiency.
The paper tackles the problem of efficient action recognition on resource-constrained wearable devices by introducing an adaptive spatiotemporal sampling scheme inspired by human vision, which speeds up inference with a tolerable accuracy loss on EPIC-KITCHENS and UCF-101 datasets.
Adaptive sampling that exploits the spatiotemporal redundancy in videos is critical for always-on action recognition on wearable devices with limited computing and battery resources. The commonly used fixed sampling strategy is not context-aware and may under-sample the visual content, and thus adversely impacts both computation efficiency and accuracy. Inspired by the concepts of foveal vision and pre-attentive processing from the human visual perception mechanism, we introduce a novel adaptive spatiotemporal sampling scheme for efficient action recognition. Our system pre-scans the global scene context at low-resolution and decides to skip or request high-resolution features at salient regions for further processing. We validate the system on EPIC-KITCHENS and UCF-101 datasets for action recognition, and show that our proposed approach can greatly speed up inference with a tolerable loss of accuracy compared with those from state-of-the-art baselines. Source code is available in https://github.com/knmac/adaptive_spatiotemporal.