Multi-example multi-label learning method for video surveillance application of safe city

Abstract

The invention discloses a multi-example multi-label learning method for a video surveillance application of a safe city. The invention obtains a multi-example multi-label data set of video surveillance of a safe city, and taps the internal connection between the multi-example data and the multi-tag data to predict the new video surveillance so as to determine the possible multiple security and traffic conditions implied in the area where the new video surveillance is located. The invention mainly contributes to two aspects, which firstly adopts a layered label strategy to solve the problem ofa large number of labels, thereby achieving the goal of retaining the integrity of multiple tags without losing the associated information between the labels, and secondly induces the convolutional neural network into the video surveillance network of a safe city at the first time, thereby fully deep learning the correlation between examples by taking advantage of the convolutional neural network,and fully exploring the information between the examples.

Description

technical field [0001] The invention belongs to the technical field of computer science and multi-instance multi-label learning, and relates to a multi-instance multi-label learning method for safe city video surveillance applications. Background technique [0002] Building a safe city is the primary goal of building a harmonious society. The improvement of urban traffic and public security management is the top priority of building a safe city. There are still many problems in building a safe city, and there are still many areas that can be improved, such as video surveillance networks. . Today's urban video surveillance network has become an important tool for urban management. However, many video data are unlabeled and scattered. From these data, managers cannot know which parts of the city need to be diverted and which parts of the city need to be rectified. The information obtained by data mining is not available. Managers cannot obtain the areas that need to be centr...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the problems that the traditional multi-instance and multi-label learning algorithm is difficult to learn the internal relationship between high-level features and the amount of calculation is too large, the present invention provides a multi-instance and multi-label learning method for video surveillance applications in safe cities

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