DCF tracking confidence evaluation and classifier updating method based on neural network

Abstract

The invention relates to a DCF tracking confidence evaluation and classifier updating method based on a neural network, and belongs to the technical field of computer vision. The method comprises thefollowing steps that firstly, a small-scale convolutional neural network of a response graph analysis network is designed and trained; in correlation filtering tracking, after convolution is carried out on a classifier and features of a search area, a generated response graph is input into the network, and output serves as a tracking confidence score of the classifier. When a score is lower than preset low confidence threshold value a target is severely disturbed so that update is stopped, thereby preventing a target model from being polluted, adaptively adjusting the updated learning rate andtime interval by a confidence score, and determining that the appearance of the target is in a highly similar state when a classifier obtains a higher confidence score for continuous multiple frames,thereby improving the updating interval to alleviate an overfitting phenomenon. the adaptability of correlation filtering tracking to interference factors such as illumination change, shielding and visual field can be remarkably enhanced, and the space and time efficiency is improved.

Description

technical field [0001] The present invention relates to a DCF (Discrimitive Correlation Filters, discriminative correlation filter) tracking confidence evaluation and classifier update method based on neural network, in particular to a DCF tracking confidence evaluation and classifier based on response graph analysis convolutional neural network The updated method belongs to the technical field of computer vision. Background technique [0002] Moving object tracking technology is one of the important research fields of computer vision, and has been widely used in the fields of automatic driving, human-computer interaction, security monitoring and so on. [0003] At present, the challenge of moving target tracking technology is: how to maintain accurate and real-time tracking under the influence of complex interference factors such as changes in background lighting conditions, occlusion, fast motion, out of view, posture shape changes, and size changes. [0004] In the prior...

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Problems solved by technology

[0008] The purpose of the present invention is to solve the problem of high interference and low efficiency when dealing with moving target tracking tasks due to the lack of tracking confidence evaluation measures in the existing correlation filter tracking method in the field of computer vision, which affects the recognition accuracy and real-time performance. Technical problem, a neural network-based DCF tracking confidence evaluation and classifier update method is proposed

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