Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface. Although great progress has been made recently, fast and robust hand gesture recognition remains an open problem, since the existing methods have not well balanced the performance and the efficiency simultaneously. To bridge it, this work combines image entropy and density clustering to exploit the key frames from hand gesture video for further feature extraction, which can improve the efficiency of recognition. Moreover, a feature fusion strategy is also proposed to further improve feature representation, which elevates the performance of recognition. To validate our approach in a "wild" environment, we also introduce two new datasets called HandGesture and Action3D datasets. Experiments consistently demonstrate that our strategy achieves competitive results on Northwestern University, Cambridge, HandGesture and Action3D hand gesture datasets. Our code and datasets will release at https://github.com/Ha0Tang/HandGestureRecognition.
翻译:虽然最近取得了巨大进展,但快速而有力的手势识别仍然是一个尚未解决的问题,因为现有方法没有同时很好地平衡性能和效率。为了弥合这一难题,这项工作将图像的渗透和密度组合结合起来,以便利用手势视频中的关键框架进一步提取特征,从而提高识别效率。此外,还提议了一项特征聚合战略,以进一步改进特征代表,从而提升识别的性能。为了验证我们在“边缘”环境中的做法,我们还引入了两个新的数据集,即手动Gesture和Action3D数据集。实验不断表明,我们的战略在西北大学、剑桥、手动和动作3D手势数据集中取得了竞争性的结果。我们的代码和数据集将在https://github.com/Ha0Tang/HandGestureRecogition上发布。