Rotor operation flying robot target tracking method and system

Abstract

The invention belongs to the technical field of visual target tracking, and discloses a rotor operation flying robot target tracking method and system, and the method comprises the steps: taking a Siamfc framework as the basis, introducing Resnet50 as a feature extraction network through offset learning, enabling the network to learn more semantic information, and coping with the appearance changeof a target; according to the tracking network, a target scale estimation module is newly added on the basis of a classification discriminator, an IOU of a target bounding box and a target real box can be predicted, the target bounding box is accurately predicted, iterative correction is carried out on the bounding box through reverse gradient, and thus the network can accurately predict the scale change of a target; output of different layers of the network is fused by utilizing Resnet50 multi-layer feature output and adopting a residual fusion strategy, so that the robustness of the algorithm is further improved, the network performance is improved, the discrimination capability of the network to a small target is guaranteed, and finally, accurate tracking of the target is realized.

Description

technical field [0001] The invention belongs to the technical field of visual target tracking, and in particular relates to a target tracking method and system for a rotor-operating flying robot. Background technique [0002] At present, visual target tracking is an important research direction in computer vision, and has a wide range of applications, such as: video surveillance, human-computer interaction, unmanned driving, etc. In the past 20 to 30 years, the visual target tracking technology has made great progress, especially in the last two years, the target tracking method using deep learning has achieved satisfactory results, making the target tracking technology a breakthrough. [0003] Target tracking technology has a very rich application in the field of UAVs. The automatic detection and tracking technology system of battlefield targets has become the basis for UAVs to realize situational awareness and precise strikes on the battlefield. The airborne computer furt...

AI Technical Summary

Problems solved by technology

[0006] The difficulty in solving the above problems and defects is: by introducing a deep network such as ResNet50, in theory, the feature extraction network can learn more abundant target information. This kind of performance will degrade the performance of the tracking algorithm

In addition, the prediction of the target scale module is a complex task, which requires the network to learn more semantic information rather than some shallow feature information. Therefore, a deeper feature extraction network is also required, and an effective target scale estimation module is also designed. Many factors need to be considered, such as how to embed into the tracking network, the design of the receptive field of the network, and how to fuse features. A scale estimation module that does not match the target discrimination and classification module will lead to a decrease in performance

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