关键词: NB-IoT, 监控视频, 变分辨率, 视频编码, 人脸检测

Abstract:

In response to the demand of wide coverage and massive access,low bandwidth and low power consumption is an important research direction to solve this problem.In smart cities,security monitoring and other application areas,video surveillance based on the region of interest of the face are particularly important.It is a feasible direction to realize the extraction of scene elements,transmission of key information with very low bandwidth and the application of video technology in the Internet of things environment through the strategy of multi-stream differential coding.By designing a face-oriented variable resolution hybrid coding algorithm,the bandwidth could be saved and the power consumption could be reduced greatly,the access requirements of narrowband Internet of things could be met.Through the face detection algorithm based on the deep learning Caffe framework,the face region of interest was acquired in key frames,and the face image was encoded with high resolution.By designing the code rate adaptive allocation algorithm,the bandwidth was utilized rationally,and the encoded face information and the full background content were distinguished.The encoded mixed code stream information was transmitted through the narrowband; the key frame-based face enhancement decoding algorithm was adopted at the receiving end to obtain a partial HD high-definition monitoring picture.Experiments show that when the video encoded by the proposed method is transmitted in a narrow band whose transmission rate is 120~160 kbit/s,the face image can maintain the same definition as the original HD monitoring acquisition end,which has strong practicability.

Key words: NB-IoT, surveillance video, variable resolution, video coding, face detection