Relevant filtering opposite-thrust target tracking method with adaptive scale

Abstract

The invention provides a relevant filtering opposite-thrust target tracking method with an adaptive scale. The method comprises: an initial position and an initial scale of a to-be-tracked target in a video frame are determined, and convolution feature graphs of different layers are extracted respectively by using the initial position as the center and using a deep convolutional neural network; for the extracted convolution feature graph of each layer, tracking is carried out by using a kernel-correlation filtering tracking method to obtain a tracking result; all tracking results are combined by using an adaptive hedging algorithm to obtain a final tracking result as a final position of the to-be-tracked target, so that the to-be-tracked target in the video frame can be localized; after obtaining of the final position of the to-be-tracked target, a final scale of the to-be-tracked target is estimated by using a scale pyramid strategy; and after obtaining of the final position and the final scale of the to-be-tracked target, a to-be-tracked target image block is extracted based on the final scale by using the final position as a center and each kernel-correlation filtering tracking method is trained again to update a coefficient and a template.

Description

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AI Technical Summary

Benefits of technology

This technology improves how well it tracks objects on different scales without changing their size or positioning themselves accurately for better accuracy. It uses both methods together with an improved version called sigma filter (a type of mathematical algorithm) to achieve this purpose effectively.

Problems solved by technology

Technological Problem addressed in this patents relates to improving object track detection techniques for various applications like autonomous vehicles. Current systems rely heavily relying upon traditional approaches involving correlated filters, including those used for visual sensors. While these existing technologies provide accurate results, they also suffer from limitations due to their reliance on specific training data sets.

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