基于外观相似性更新的相关滤波跟踪算法

doi:10.12305/j.issn.1001-506X.2022.01.16

Abstract

Abstract:

To solve the problem of kernel correlation filter (KCF) algorithm, which is prone to model drift in complicated environments, an algorithm using adaptive updating target tracking algorithm based on KCF(AUKCF) is proposed. This algorithm firstly makes multi-peak judgment on the response, then uses the saliency detection to relocate the target for the multi-peak phenomenon to reduce the model drift. To ensure the accuracy of the saliency detection, the redetection method is applied to calibrate the saliency detection result. Finally, Spearman correlation, which reduces model degradation and improves update efficiency is used to determine whether the target has problems such as occlusion and serious deformation, and it is determined whether to update the model according to the results of Spearman's correlation. Tested on the OTB2015 benchmark, the experimental results show that the accuracy and success rate of the AUKCF and KCF algorithms are increased by 14% and 11.8%, respectively. Compared with the current deep learning algorithm, the AUKCF algorithm has a simpler model and lower requirements for equipment, the real-time performance can reach 93.84 fps.

Key words: target tracking, saliency detection, Spearman correlation, model degradation, model update

CLC Number:

TP139

Cheng FANG, Wen LU, Jingying JI, Yumeng SONG, Feifei LIANG, Zhiwei LUO. Correlation filter-tracking algorithm based on appearance similarity update[J]. Systems Engineering and Electronics, 2022, 44(1): 117-126.

Figures/Tables 6

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算法名称	显著性检测调节	自适应更新
KCF	-	-
KCFup	-	√
KCFsd	√	-
AUKCF	√	√

算法	相机抖动(准确率)	超出视野(准确率)	光照变化(准确率)	快速运动(准确率)	遮挡(准确率)	尺度变化(准确率)	准确率均值	准确率加权	准确率合并方差	EAO
AUKCF	0.522 8	0.464 6	0.476 6	0.461 0	0.425 7	0.363 5	0.452 4	0.465 4	0.469 0	0.119 9
DensSiam^[42]	0.468 7	0.472 2	0.486 9	0.441 5	0.420 2	0.348 5	0.439 7	0.445 7	0.456 5	0.167 8
PBTS^[43]	0.389 0	0.386 8	0.361 4	0.347 3	0.312 7	0.335 8	0.355 5	0.367 8	0.375 3	0.147 2
MIL^[44]	0.377 0	0.410 7	0.380 3	0.360 0	0.358 0	0.291 4	0.362 9	0.371 4	0.385 2	0.113 8
BST^[45]	0.242 0	0.308 2	0.270 6	0.236 8	0.260 8	0.236 8	0.259 2	0.262 5	0.272 6	0.113 7
struck2011^[46]	0.437 3	0.409 0	0.420 2	0.408 3	0.378 0	0.312 4	0.394 2	0.402 3	0.410 4	0.093 0
BDF^[47]	0.379 0	0.361 5	0.400 5	0.355 7	0.378 8	0.309 4	0.364 1	0.361 2	0.365 2	0.091 5
Matflow	0.402 9	0.416 9	0.405 5	0.354 9	0.360 8	0.314 1	0.375 8	0.384 3	0.398 2	0.090 0
MRSNCC	0.318 1	0.357 1	0.359 9	0.277 0	0.285 1	0.295 1	0.315 4	0.318 5	0.328 2	0.079 7
DSST^[37]	0.407 5	0.418 2	0.474 8	0.354 6	0.349 7	0.301 4	0.384 4	0.384 9	0.396 7	0.078 0
IVT^[48]	0.377 2	0.431 4	0.421 9	0.325 8	0.361 6	0.330 1	0.374 7	0.378 8	0.396 4	0.075 0
CPOINT	0.295 9	0.326 3	0.281 1	0.261 7	0.283 9	0.310 6	0.293 2	0.300 4	0.308 8	0.069 4
L1APG^[49]	0.420 3	0.467 1	0.391 0	0.378 5	0.382 1	0.299 7	0.389 8	0.407 8	0.428 3	0.068 6
FragTrack	0.392 2	0.403 7	0.373 5	0.348 0	0.325 0	0.328 4	0.361 8	0.374 3	0.390 1	0.068 2
Matrioska^[50]	0.431 3	0.437 5	0.428 9	0.383 5	0.364 8	0.295 8	0.390 3	0.402 0	0.416 0	0.064 2

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Correlation filter-tracking algorithm based on appearance similarity update

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