Human Detection via Image Denoising for 5G‐Enabled Intelligent Applications

Abstract

5G technology strongly supports the development of various intelligent applications, such as intelligent video surveillance and autonomous driving. And the human detection technology in intelligent video surveillance has also ushered in new challenges. A number of video images will be compressed for efficient transmission; the resulting incomplete feature representation of images will drop the human detection performance. Therefore, in this work, we propose a new human detection method based on compressed denoising. We exploit the quality factor in the compressed image and incorporate the pixel_shuffle inverse transform based on FFDNet to effectively improve the performance of image compression denoising, then HRNet and HRFPN are used to extract and fuse high-resolution features of denoised images, respectively, to obtain high-quality feature representation, and finally, a cascaded object detector is used for classification and bounding box regression to further improve object detection performance. At last, the experimental results on PASCAL VOC show that the proposed method effectively removes the compression noise and further detects human objects with multiple scales and different postures. Compared with the state-of-the-art methods, our method achieved better detection performance and is, therefore, more suited for human detection tasks.