Non-overlapping vision field multi-camera monitoring network topology self-adaptation learning method

Abstract

The invention provides a non-overlapping vision field multi-camera monitoring network topology self-adaptation learning method, and relates to the field of computer vision. A weighted directed graph G=<V, E and W> is used, and the topology of a monitoring network is represented. According to the non-overlapping vision field multi-camera monitoring network topology self-adaptation learning method, the leaving position and the entering position of a target in a single-camera vision field are used as topological nodes V, and a Gaussian mixture model is utilized for modeling. The cross-correlation function computing method based on united surface similarity is provided, the connectivity of a certain pair of nodes is judged through a cross-correlation function, and therefore an edge set E is obtained. As for the connected node pair, transfer time distribution is calculated through the standardization cross-correlation function. Mutual information of the node pair is utilized for representing the transfer probability of the nodes, and therefore the weight set W is obtained. According to the non-overlapping vision field multi-camera monitoring network topology self-adaptation learning method, the false connection removal strategy is provided for removing probable false connection in the topology, the topology self-adaptation updating strategy is provided for ensuring the higher robustness of the topological structure to environmental changes.

Description

technical field [0001] The invention belongs to the field of computer vision, specifically relates to the field of intelligent monitoring, in particular to a method for self-adaptive learning of network topology for multi-camera monitoring without overlapping fields of view. Background technique [0002] With the development of camera monitoring technology, monitoring a large area has become an important means to ensure the safety of people's lives and property. However, for a monitoring situation with a large area, it is unrealistic to use cameras to cover all the monitoring areas. Therefore, the method of covering key areas is usually used to build a multi-camera surveillance system with non-overlapping fields of view. Compared with the traditional single-camera surveillance system or overlapping multi-camera surveillance system, the non-overlapping multi-camera surveillance system is more difficult to continuously track the target because its observation targets are disc...

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However, face recognition technology has certain requirements for imaging accuracy and angle, so it is not suitable for promotion

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