CNN Based Posture-Free Hand Detection
This work addresses hand detection for applications requiring speed, but it is incremental as it builds on existing CNN approaches.
The authors tackled the problem of hand detection by proposing a shallow CNN network that is fast and insensitive to translation and hand poses, achieving 93.9% accuracy and faster speed than state-of-the-art methods.
Although many studies suggest high performance hand detection methods, those methods are likely to be overfitting. Fortunately, the Convolution Neural Network (CNN) based approach provides a better way that is less sensitive to translation and hand poses. However the CNN approach is complex and can increase computational time, which at the end reduce its effectiveness on a system where the speed is essential.In this study we propose a shallow CNN network which is fast, and insensitive to translation and hand poses. It is tested on two different domains of hand datasets, and performs in relatively comparable performance and faster than the other state-of-the-art hand CNN-based hand detection method. Our evaluation shows that the proposed shallow CNN network performs at 93.9% accuracy and reaches much faster speed than its competitors.