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Disjoint-view object matching method based on corrected weighted bipartite graph

A non-overlapping view and target matching technology, applied in the field of non-overlapping view target matching based on modified weighted bipartite graph, can solve problems such as large posterior probability

Active Publication Date: 2014-09-24
SOUTHEAST UNIV
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AI Technical Summary

Problems solved by technology

Construct a weighted bipartite graph, and solve the maximum a posteriori probability problem by solving the maximum weight matching of the bipartite graph

Method used

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  • Disjoint-view object matching method based on corrected weighted bipartite graph
  • Disjoint-view object matching method based on corrected weighted bipartite graph
  • Disjoint-view object matching method based on corrected weighted bipartite graph

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Embodiment Construction

[0023] figure 1 The system flow chart of the non-overlapping view target matching method based on the modified weighted bipartite graph is given: the maximum posterior probability framework is used to describe the non-overlapping view target matching problem, and the target is comprehensively considered when constructing the maximum posterior probability frame. Observation models and monitoring network spatiotemporal constraints. The invention extracts the main color feature and spatial texture feature of the target, and constructs a target observation model with strong robustness to illumination differences and environmental changes. On the premise that the topology of the monitoring network is known, the invention obtains the space-time constraints of the monitoring network from the topology. According to the space-time constraints of the monitoring network, the monitoring network topology is divided into multiple minimum units according to the principle of independence and...

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Abstract

The invention provides a disjoint-view object matching method based on a corrected weighted bipartite graph. The method relates to the field of computer vision. The method expresses a disjoint-view object matching problem as a maximum posterior probability problem, so that an object observation model and time-space constraints of a surveillance network are combined, and the maximum posterior probability problem is resolved through solving the maximum weight matching of a weighted bipartite graph. To solve the problem that construction of a common weighted bipartite graph is liable to introduction of incorrect matching, the method provides a corrected weighted bipartite graph construction method based on an adaptive threshold, so that incorrect matching is prevented from being introduced during construction of the weighted bipartite graph as much as possible. Aimed at the defect of a conventional KM method that the amount of computation is too large during large-scale weighted bipartite graph matching problem solving, the method brings forward a MH sampling-based method for approximating and solving the maximum weight matching of the weighted bipartite graph, so that a disjoint-view object matching relationship is obtained.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to the field of intelligent monitoring, and in particular to a non-overlapping visual field target matching method based on a modified weighted bipartite graph. 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, it is unrealistic to use cameras to cover all the surveillance areas for surveillance applications with larger areas. Therefore, the method of key area coverage is usually used to build a multi-camera surveillance system including non-overlapping fields of view. For non-overlapping viewshed monitoring networks, only by determining the matching relationship of targets in different viewports can we better understand the behavior of targets in the entire monitoring scene. However, interfering factors such as illumination changes an...

Claims

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Application Information

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IPC IPC(8): H04N7/18G06T7/00
Inventor 林国余杨彪张宇歆张为公戴栋
Owner SOUTHEAST UNIV
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