Wireless sensor node optimization selection method orientated at visual tracking

Abstract

The invention relates to a wireless sensor node optimization selection method orientated at visual tracking. The wireless sensor node optimization selection method orientated at visual tracking comprises the following steps that whether triggering conditions meeting node selection exist at the current time point is checked; each video sensing node reconstructs image frames, a motion target is extracted by adopting a self-adaptation Gaussian mixture background model, the sizes of target blocks are counted and a hue histogram model of the target blocks is built; the nodes achieve target motion prediction by utilizing unscented kalman filtering at the same time; relation among targets in different image planes is achieved through homography conversion among node visual angles, and the corresponding relationship among multiple adjacent targets is accurately determined by combing the matching of the hue histogram; a confidence coefficient model of the nodes is built according to the sizes of the target blocks and indetermination characteristic of tracking, and the node with the largest determination degree is selected as the best node and takes over a subsequent tracking task. The wireless sensor node optimization selection method orientated at visual tracking effectively overcomes partial shielding in the motion process of the targets, has good target tracking performance, and meanwhile has low communication expenses and calculation complexity.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a visual tracking-oriented wireless sensor node optimization selection method, which is suitable for application fields such as wireless sensor network intelligent perception, monitoring and early warning. Background technique [0002] With the development of wireless sensor network technology, microprocessor technology and CMOS imaging technology, the wireless video sensor network with multi-view video acquisition and perception capabilities has been developed rapidly. Compared with traditional wireless sensor nodes, video sensor nodes have multi-view and multi-directional image perception and processing capabilities, which put forward higher requirements for network energy consumption, communication bandwidth, algorithms, and collaborative processing of node information. Due to resource constraints such as energy supply, communication bandwidth, computing and s...

AI Technical Summary

Problems solved by technology

Its disadvantage is that it requires prior knowledge of the relative positional relationship of each sensor node and the overlapping area of ​​view angle.

Although this method has high tracking accuracy, its calculation is complicated and it is difficult to meet the real-time requirements

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