Multi-Person Action Recognition in Microwave Sensors

Abstract

The usage of surveillance cameras for video understanding, raises concerns about privacy intrusion recently. This motivates the research community to seek potential alternatives of cameras for emerging multimedia applications. Stepping to this goal, a few researchers have explored the usage of Wi-Fi or Bluetooth sensors to handle action recognition. However, the practical ability of these sensors is limited by their frequency band and deployment inconvenience because of the separate transmitter/receiver architecture. Motivated by the same purpose of reducing privacy issues, we introduce a latest microwave sensor for multi-person action recognition in this paper. The microwave sensor works at 77GHz ∼ 80GHz band, and is implemented with both transmitter and receiver inside itself, thus can be easily deployed for action recognition. Although with its advantages, two main challenging issues still remain. One is the difficulty of labelling the invisible signal data with embedding actions. The other is the difficulty of cancelling the environment noise for high-accurate action recognition. To address the challenges, we propose a novel learning framework by designed original loss functions with the considerations on weakly-supervised multi-label learning and attention mechanism to improve the accuracy for action recognition. We build a new microwave sensor data set, and conduct comprehensive experiments to evaluate the recognition accuracy of our proposed framework, and the effectiveness of parameters in each component. The experiment results show that our framework outperforms the state-of-the-art methods up to 14% in terms of mAP.