A robust context attention network for human hand detection

Abstract

Current state-of-the-art approaches for human hand detection have achieved great success by making good use of multiscale and contextual information but still remain unsatisfactory for hand detection, especially in complex scenarios. The main reason is that there are some parts similar to human hands, such as wrists, faces and feet. Simply using contextual information makes it difficult to address these problems. In this paper, we propose a Context Attention Feature Pyramid Network (CA-FPN) for human hand detection. In this method, a novel Context Attention Module (CAM) is inserted into the feature pyramid networks. The CAM is designed to capture relative contextual information for hands and build long-range dependencies around hands. Our CA-FPN can achieve state-of-the-art results on two public hand detection datasets: the Oxford and Vision for Intelligent and Applications (VIVA) datasets. Furthermore, the inference time of our CA-FPN is approximately 8.5 FPS on one TITAN X GPU, indicating that it can be used in real-time applications. Besides, the CAM helps improve head detection on the HollywoodHeads dataset, demonstrating its robustness in other detection tasks. The code has been made available at https://github.com/IC-LAB/CA-FPN.