Video detection method for street order events based on deep learning and motion consistency

Abstract

A video detection method for street order events based on deep learning and motion consistency, involving the fields of artificial intelligence and computer vision. The method steps of the present invention are: 1) algorithm framework: 2) target detection; 3) motion consistency calculation; 4) event judgment. Compared with the prior art, the present invention designs the target detection deep learning network, trains the scene recognition model, and calculates the motion information in the scene to analyze its behavior state, and uses the target detection technology in the static video frame and the dynamic video frame in the field of video intelligent analysis. Combining medium and target behavior analysis technology, multi-condition joint determination of events, designing a detection system for out-of-store business events and road-occupied business events, and accurately and quickly complete the automatic detection of events.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and computer vision, and is an intelligent detection method for street order events based on image processing technology and video analysis technology and applied in urban monitoring scenes. Background technique [0002] Wen Jun, in his doctoral thesis "Research on Outdoor Scene Understanding Based on Deep Convolutional Neural Network" in March 2016, disclosed that based on the DCNN algorithm, he studied dynamic object classification, semantic segmentation and joint object detection around scene segmentation and scene recognition. Scene Understanding Techniques with Semantic Segmentation. [0003] Firstly, for the classification of moving target objects in videos, a dynamic target classification method based on multi-task spatial pyramid pooling DCNN is proposed. High-level convolutional features are robust to translation, viewing angle changes, lighting, partial occlusion, etc. of moving ...

AI Technical Summary

Problems solved by technology

[0011] The problem with the above-mentioned traditional algorithms is that in terms of identifying and understanding urban monitoring scenes, traditional algorithms are difficult to meet the needs.

These factors require the algorithm to have super generalization ability and accuracy. Traditional algorithms cannot meet these requirements on a theoretical basis. Even if they are applied, it is easy to cause false detection and missed detection of events.

[0012] The problem with the above algorithm based on deep learning is that based on the deep learning algorithm, a method with strong generalization ability can be designed to solve the problem of various event forms, but it also has high requirements for the design of the network model

Therefore, the simple application of static information of image frames has low accuracy and many false detections and missed detections in target detection and scene recognition.

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