Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other.

该论文提出了基于深度学习的手部检测方法，在同一时间内对手部进行检测和旋转校准。借助上下文感知建议生成算法以保证召回率，并设计了一个CNN来显式处理物体旋转问题，实验证明该方法在人机交互等领域的应用优于现有方法。

利用卷积神经网络实现手部联合检测和旋转估计