Adaptive Correction Tracking Algorithm Based on Detector and Locator Fusion

doi:10.16182/j.issn1004731x.joss.21-1273

Abstract

Abstract:

In order to avoid tracking failure caused by occlusion, rotation and other factors in complex dynamic scenes, an adaptive correction tracking algorithm based on detector and locator fusion is proposed. The locator trains a convolutional neural network (CNN) filter for location estimation by extracting the deep features of target. The CNN filter adds two layers of shallow features to the three layers of the convolution features of original CF2 algorithm, which enhances the extraction of target texture information. The detector calculates the confidence score by extracting histogram of oriented gradient(HOG) feature of target and combining the context information. The average peak-to-correlation energy (APCE) and maximum response value of current frame are compared separately with the historical average to comprehensively judge whether the tracking fails are due to occlusion and other factors. If the tracking fails, combine the detector to relocate the target, otherwise estimate the scale of the target. Update the model when the model has high confidence. The experimental results show that the distance accuracy and overlap accuracy of the algorithm are good.

Key words: convolution features, detector, tracking, adaptive correction, high confidence

CLC Number:

TP391.41

Yecai Guo, Cheng Liu. Adaptive Correction Tracking Algorithm Based on Detector and Locator Fusion[J]. Journal of System Simulation, 2023, 35(4): 709-720.

Figures/Tables 10

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Table 1

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算法	BC	DEF	FM	IPR	IV	LR	MB	OCC	OV	OPR	SV
本文	0.883	0.810	0.822	0.884	0.884	0.760	0.817	0.787	0.707	0.841	0.816
HDT	0.844	0.821	0.802	0.844	0.820	0.740	0.794	0.774	0.663	0.805	0.811
CF2	0.843	0.791	0.792	0.854	0.817	0.763	0.797	0.767	0.677	0.807	0.802
siamFC	0.705	0.661	0.722	0.742	0.703	0.749	0.676	0.661	0.664	0.733	0.731
samf	0.689	0.686	0.667	0.721	0.714	0.678	0.677	0.728	0.627	0.738	0.710
fDSST	0.779	0.611	0.690	0.727	0.746	0.586	0.700	0.640	0.578	0.666	0.667
KCF	0.713	0.617	0.620	0.701	0.719	0.505	0.618	0.630	0.501	0.677	0.639
DSST	0.715	0.561	0.569	0.708	0.738	0.568	0.608	0.614	0.480	0.658	0.648

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