On combining deep features and machine learning for automatic edge pedestrian detection task

Abstract

Due to the restriction of cloud computing for video processing and distribution in networks, this paper designs an machine learning‐based automatic edge detection framework for video processing task in the Internet of Things (IoT) networks. Since the video processing algorithm is complex and consumes massive computing resources, it can reduce the network transmission to put the video processing model on the edge servers, uploading the processing results from the edge servers to the cloud computing center. Obviously, the processing result is much smaller than the original video, which can implement real‐time transmission. On the edge servers, the video is processed by utilizing a faster region convolutional neural network (Faster R‐CNN) which integrates feature extraction with classification. The experiments, performed on several pedestrian flow detection datasets, demonstrate the effectiveness of the designed automatic edge detection framework.