Gesture recognition has always been a research hotspot in the field of human‐computer interaction. Its purpose is to realize the natural interaction with the machine by recognizing the semantics expressed by gesture. In the process of gesture recognition, the occlusion of gesture is an inevitable problem. In the process of gesture recognition, some or even all of the gesture features will be lost due to the occlusion of the gesture, resulting in the wrong recognition or even unrecognizability of the gesture. Therefore, it is of great significance to study gesture recognition under occlusion. The single shot multibox detector (SSD) algorithm is analyzed, and the front‐end network is compared. Mobilenets is selected as the front‐end network, and the Mobilenets‐SSD network is improved. In tensorflow environment, based on the improved network model, the self‐occlusion gesture and object occluding gesture are trained in color map, depth map, and color and depth fusion respectively. The recognition models of self‐occlusion gestures and object‐occlusion gestures in color map, depth map, and color and depth fusion are obtained. And compare and analyze the learning rate, loss function, and average accuracy of various models obtained for occlusion gesture recognition.

中文翻译：

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基于改进的SSD的遮挡手势识别

手势识别一直是人机交互领域的研究热点。其目的是通过识别手势表达的语义来实现与机器的自然交互。在手势识别过程中，手势的遮挡是不可避免的问题。在手势识别过程中，由于遮挡手势，部分或全部手势特征将丢失，从而导致手势识别错误或什至无法识别。因此，研究遮挡下的手势识别具有重要意义。分析了单发多盒检测器（SSD）算法，并比较了前端网络。选择Mobilenets作为前端网络，并改进了Mobilenets-SSD网络。在张量流环境中，基于改进的网络模型，自遮挡手势和对象遮挡手势分别在颜色图，深度图以及颜色和深度融合中进行训练。获得了颜色图，深度图以及颜色和深度融合中的自我遮挡手势和对象遮挡手势的识别模型。并比较和分析为遮挡手势识别而获得的各种模型的学习率，损失函数和平均准确性。