Gesture recognition based on multi-modal feature weight

With the continuous development of sensor technology, the acquisition cost of RGB-D images is getting lower and lower, and gesture recognition based on depth images and Red-Green-Blue (RGB) images has gradually become a research direction in the field of pattern recognition. However, most of the current processing methods for RGB-D gesture images are relatively simple, ignoring the relationship and influence between its two modes, and unable to make full use of the correlation factors between different modes. In view of the above problems, this paper optimizes the effect of RGB-D information processing by considering the independent features and related features of multi-modal data to construct a weight adaptive algorithm to fuse different features. Simulation experiments show that the method proposed in this paper is better than the traditional RGB-D gesture image processing method and the gesture recognition rate is higher. Comparing the current more advanced gesture recognition methods, the method proposed in this paper also achieves higher recognition accuracy, which verifies the feasibility and robustness of this method.

Automated summary

This study optimized the processing of RGB-D information by constructing a weight adaptive algorithm, improving gesture recognition rate. Compared to traditional methods, this approach achieved higher recognition accuracy, demonstrating its feasibility and robustness.