Efficient SIFT Feature Transmission for Machine Vision: A Video Coding Approach

This paper investigates the application of video coding techniques in machine vision, specifically focusing on partial transmission and reconstruction of SIFT features. The paper begins by outlining the importance and application scenarios of SIFT features in computer vision. It then proposes a method for partial transmission of SIFT features based on the H.264 video coding standard.

The proposed method involves dividing video frames into multiple regions and extracting SIFT features from each region. During video encoding, only a portion of the SIFT feature vectors from each region is transmitted, significantly reducing the amount of data transferred. By decoding and reconstructing these partial SIFT feature vectors, the complete SIFT feature vectors can be recovered, enabling accurate feature recognition and matching in machine vision applications.

Experimental results demonstrate that the proposed method can reduce data transmission volume by up to 90% compared to traditional SIFT feature transmission methods, while maintaining high feature recognition and matching accuracy. Therefore, this method holds significant application value and can play a crucial role in machine vision, IoT, and other fields.

In the related work section of your paper, you can compare and analyze the proposed method with other video coding and feature transmission techniques to highlight its advantages and innovative aspects. Additionally, you can discuss the limitations and potential improvements of this method in real-world applications, providing valuable insights for future research.