Moving target detecting and tracking algorithm evaluation method based on data set compensation

Abstract

The invention discloses a moving target detecting and tracking algorithm evaluation method based on data set compensation. The moving target detecting and tracking algorithm evaluation method automatically adds quantification factors of eight performance interference factors for data sets, and allows the user to select variation factors when evaluating an algorithm conveniently; the moving target detecting and tracking algorithm evaluation method can customize weight sizes of the variation factors according to degree of sensitivity of a specific evaluated algorithm to specific variation factors, so that comprehensive quantification factors corresponding to the data sets truly reflect the algorithm application environments, and finally the objectiveness of evaluation results is ensured; at last, since the comprehensive quantification factors are comprehensive quantification embodiments of the variation factors affecting algorithm performances in the corresponding data sets, the comprehensive quantification factors of the data sets are adopted for performing weighted compensation for threshold values used for carrying out success determination of the evaluation index precision OverlapP, can show different influences of the data sets with different variation factors on the algorithm performances in the evaluation result, can simulate results of the algorithm under some interference conditions, and provides certain guidance for the further improvement direction of the algorithm.

Description

technical field [0001] The invention relates to the field of video image processing, in particular to an evaluation method of a moving target detection and tracking algorithm based on data set compensation. Background technique [0002] With the far-reaching development in the field of computer vision, target detection and tracking algorithms emerge in an endless stream, and automatic target tracking systems are widely used in our lives, which also brings a variety of complex application environments, resulting in target The imaging forms are different, which brings difficulties to the detection and tracking of targets by computer vision algorithms. The evaluation of target detection and tracking algorithm and its system is the most basic stage in the development of automatic target tracking system. The evaluation of the algorithm can not only compare and select the existing algorithms, but also clarify the further development direction of the algorithm and promote the furt...

AI Technical Summary

Problems solved by technology

[0003] The current detection and tracking algorithm evaluation methods are generally based on the data set of the manually calibrated target position as the carrier, which is highly dependent on the data set, but the results of the algorithm evaluation often cannot reflect the impact of different data sets on the operation of the algorithm from the digital quantity The performance impact of the result change factors (hereinafter referred to as change factors) on the algorithm results, among which the external environment change factors are occlusion, illumination, similar interference, target loss and target self change factors scale change, motion speed, rigid body non-rigid body, small target etc. are currently found to be the main changing factors that strongly interfere with the performance of the algorithm

At the same time, due to the large amount of work required to manually calibrate the data, the further expansion of the data set also restricts the development of the evaluation accuracy.

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