Perception decision integration target detection and tracking method based on high and low perspective cooperation

By employing network time synchronization, CSPDarknet feature extraction, homography correction and geographic coordinate registration, unified geospatial metric, and multi-branch spatiotemporal graph convolutional network, the problems of perspective distortion and latency in the air-ground collaborative monitoring system were solved. This achieved high-accuracy entity re-anchoring and low-latency perception-decision consistency, thereby improving the cross-view target tracking capability of air-ground cameras.

CN122200014BActive Publication Date: 2026-07-24NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Application Number
CN202610669005.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-15
Publication Date
2026-07-24
Estimated Expiration
2046-05-15

AI Technical Summary

Technical Problem

Existing technologies in air-ground collaborative monitoring systems suffer from problems such as trajectory chain breakage caused by severe perspective distortion, system crashes caused by dead point overflow in reverse perspective mapping, and high response latency caused by the fragmentation of the perception and decision-making link. These issues make it difficult to achieve cross-perspective area mapping, accurate target tracking, and edge-decentralized state fusion.

Method used

It adopts high and low bit video stream access and temporal multimodal alignment based on network time synchronization protocol, combines target detection and deep feature extraction of CSPDarknet backbone network, cross-view spatial mapping based on homography correction and geographic coordinate registration, uses trajectory association integration with unified geospatial distance metric and bipartite graph matching, and realizes integrated generation of perception and decision through multi-branch spatiotemporal graph convolutional network ST-GCN.

Benefits of technology

It achieves uninterrupted, high-accuracy entity re-anchoring under conditions of rapid elevation and depression angle changes, solves the problems of feature collapse and soaring matching and recognition error rates in traditional systems, realizes a low-latency perception-decision consistency closed loop, and improves the cross-view area mapping anti-distortion and target accuracy tracking capabilities of air-ground camera collaboration.

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Abstract

The application discloses a kind of perception decision integration target detection and tracking method based on high-low view angle cooperation, comprising: high-low video stream access and timing multimodal alignment based on network time synchronization protocol;Based on the backbone layer network target detection and depth feature extraction of CSPDarknet;Based on homography correction and geographic coordinate registration, cross-view space mapping;Based on the track association integration of unified geographic spatial distance measurement and bipartite graph matching;Based on the perception decision integration generation of multi-branch spatio-temporal graph convolution network ST-GCN.The application can solve the problem of feature collapse and matching recognition error rate soaring caused by the dependence of existing technology on re-identification depth appearance features in response to high-low pitch angle sharp crossing, realize the effect of uninterrupted and high-precision entity re-anchor in the target appearance distortion state across the scale deformation barrier.
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