A scale self-adaptive target tracking algorithm based on kernel correlation filtering

Abstract

The invention discloses a scale self-adaptive target tracking algorithm based on kernel correlation filtering, and belongs to the field of computer vision. The method comprises the steps of selectinga first frame of to-be-tracked target, and determining as a candidate region; Extracting gradient direction histogram (HOG) characteristics of the candidate area, and carrying out cosine weighting; Performing cyclic shift on the candidate area by using a KCF algorithm to generate a training sample, calculating a response value in a frequency domain according to a position filter obtained by sampletraining and HOG characteristics extracted by the candidate sample, and updating the target position of the current frame; Taking the target position determined by the previous frame as a center to obtain a scale filter, calculating to obtain a response value, and taking the scale corresponding to the maximum response value as the target scale of the current frame; Re-extracting the sample training filter, updating the position filter and the scale filter in a linear interpolation mode, and tracking a subsequent frame; The method can be applied to the fields of intelligent video monitoring, enterprise production automation, intelligent robots and the like.

Description

technical field [0001] The invention relates to video target tracking and belongs to the field of computer vision, in particular to a scale adaptive target tracking algorithm based on kernel correlation filtering. Background technique [0002] Object tracking is one of the important research directions in the field of computer vision, and it has a wide range of applications in public security monitoring and management, medical image analysis, behavior understanding, visual navigation, etc. At present, scholars at home and abroad are mainly concerned with the tracking robustness and accuracy improvement under the conditions of similar target interference, target scale change, blurred appearance, occlusion, and real-time performance of the target tracking system in practical applications. [0003] In recent years, target tracking algorithms based on correlation filters have begun to rise, and due to their high computational efficiency, they are gradually showing superior perfo...

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Problems solved by technology

[0004] However, the above three algorithms are prone to tracking drift when the target scale changes greatly because they are mainly devoted to the improvement of target position estimation performance.

In response to this problem, Martin D et al. proposed a discriminative scale space tracking method in the article "Discriminative scale space tracking" published in Pattern Analysis and Machine Intelligence (2017, 39(3): 1561-1575), which effectively improves the scale adaptive performance. The calculation speed of the tracking algorithm is low, and it is difficult to meet the real-time requirements of the tracking system

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