| dc.description.abstract | Video surveillance is used in the investigation and handling of cases in our country. It is a great tool to trace back and restore the truth. It is also suitable for tracking the historical trajectory of past crimes. At present, license plate recognition cameras are often used in my country to quickly find the trajectory of objects, but the camera angle of view is limited. Due to factors such as cost considerations and other factors, it is impossible to fully replace and deploy license plate recognition cameras. In road sections without license plate recognition cameras, investigators still identify suspicious vehicles with the naked eye, and call multiple intersection monitor images to identify vehicles based on their appearance. Therefore, this paper refers to the vehicle re-identification technology, which uses the appearance of the vehicle to achieve the image similarity comparison of different cameras, angles and different time points, to achieve the purpose of quickly filtering the target vehicle, which should help to quickly retrieve suspicious vehicles as the first line of filtering.
However, there are some differences between the research on vehicle re-identification and the actual environment of the video surveillance system. The main target is cars, and the demand for re-identification of buses or trucks is not high. Therefore, this paper aims to fit the technology In the actual video surveillance system, the test data set is used for object detection first, and vehicle objects are extracted to simulate the recognition situation of other video surveillance systems, and the marked data such as buses and trucks are excluded to form a new data set, which is then correlated with the continuous time of images The vehicle objects are superimposed and become the dynamic data of the vehicle body related to time and space. In addition to using the technology of vehicle re-identification to extract the appearance features in the training data, this paper also designs a dynamic feature network layer, through continuous time images and vehicles. The dynamic displacement data extracts the dynamic characteristics of the vehicle body. The overall model improves the accuracy of re-identification of sedan-type vehicles through the characteristics of vehicle body dynamics and vehicle appearance. The experimental environment of this paper is based on the premise of the video surveillance system. For future use of re-identification If the technical research is also connected to the video surveillance system, it should have its reference value.
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