Abstract
Person reidentification rate has become a challenging research topic in the field of computer vision due to the fact that person appearance is easily affected by lighting, posture and perspective. In order to make full use of the continuity of video data on the time line and the unstructured relationship of features, a video person reidentification algorithm combining the neural ordinary differential equation with the graph convolution network is proposed in this paper. First, a continuous time model is constructed by using the ordinary differential equation (ODE) network so as to capture hidden information between video frames. By simulating the hidden space of the hidden variables with the hidden time series model, the hidden information between frames that may be ignored in the discrete model can be obtained. Then, the features of the generated video frames are given to the graph convolution network to reconstruct them. Finally, weak supervision is used to classify the features. Experiments on PRID2011 datasets show that the proposed algorithm can significantly improve person reidentification performance.
Highlights
In recent years, with the increasing attention to public safety and the development of video surveillance technology, more and more cameras are deployed in crowded places [1, 2]
The use of computer vision technology to automatically complete the task of intelligent monitoring system came into being [3]
The same convolution kernel is used for the convolution operation of each graph node, which can greatly reduce the number of model parameters
Summary
With the increasing attention to public safety and the development of video surveillance technology, more and more cameras are deployed in crowded places [1, 2]. The use of computer vision technology to automatically complete the task of intelligent monitoring system came into being [3]. The current face recognition technology has been relatively mature, it is often unable to obtain effective face images in the actual monitoring environment. It is very important to lock and search people with whole body information. This makes human re-recognition technology gradually become a research hotspot in the field of computer vision, which has attracted people’s extensive attention [4]
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