Pedestrian Recognition System of Wild Scene Video Streaming Based on Convolutional Neural Network

Abstract

Object detection aims at detecting the position of important objects and identifying them in still images or video frames. Pedestrians are the objects with most attention in images and videos. We study the detection and recognition technology for pedestrian objects of the wild scene video streams. Based on convolutional neural network, we design an online pedestrian recognition system. As non-rigid objects, people may exist in different poses in the image. Moreover, with the interference of complex background, lighting changes and occlusions, it will be very difficult to detect and recognize human objects. Following pedestrian detection in the images, we can conduct research on pedestrian tracking, pedestrian recognition and pedestrian behavior analysis. In addition, pedestrian detection technology also has great commercial value in video monitoring systems, intelligent traffic systems and other fields. In this paper, we propose a network model based on multiple loss functions, which combines the cross-entropy loss function with the improved monitoring signal center loss function to update the model parameters. Finally, the extracted features are more obvious, making the features of the same individual more compact. Compared to networks trained with only cross-entropy loss functions, our method significantly improves recognition accuracy and implements a pedestrian attribute recognition application based on Web technology. And compared with other similar applications, the recognition system we designed is cross-platform and lightweight. It mainly includes image acquisition, pedestrian attribute recognition and data storage modules, which is of great significance for the application of pedestrian recognition technology in the wild scenes.