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.
Highlights
With the emergence and development of 5G technology [1], it is widely used for data transmission, wireless communication, and other intelligent applications such as intelligent video surveillance and industrial Internet of Things [2,3,4]
To verify whether the improved ResNet18 can accurately estimate the value of the quality factor in the compressed image, Table 1 shows the mean square error between the value of the quality factor estimated using our method and its corresponding true value at different iterations; it can be seen that the mean square error between the estimated and true values of the compression quality factor is 0.099 at the number of iterations of 200, which is less than 0.1, the improved ResNet18 network can be considered to be able to estimate the compression quality factor accurately
In order to show that the denoising algorithm can improve the performance of human detection, the quality factor of the JPEG compression algorithm is set to 40 and applied to the test set, that is, the compression noise is implanted to imitate the distortion caused by the actual JPEG, thereby verifying the correctness of the algorithm
Summary
With the emergence and development of 5G technology [1], it is widely used for data transmission, wireless communication, and other intelligent applications such as intelligent video surveillance and industrial Internet of Things [2,3,4]. Some researchers attempt to exploit deep learning technology to solve some problems in wireless communication [5] and industrial Internet of Things [6,7,8]. Though the method based on deep learning for human detection can achieve more robust detection results than traditional methods like HOG [13] +SVM [14], and DPM [15], it still needs to further improve its detection accuracy for objects with small objects and variable scales [16, 17].
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