在线目标分类及自适应模板更新的孪生网络跟踪算法

doi:10.11959/j.issn.1000-436x.2021127

Abstract

Abstract:

Aiming at the problem that tracking algorithm of Siamese network learned the embedded features of the tracked target and the object in the offline training stage, and these embedded features often lacked the target-specific context information, which made these tracking algorithms less robust, a tracking algorithm of the Siamese network based on online target classification and adaptive template update was proposed, which used SiamRPN++ as the baseline algorithm.Firstly, a cross-correlation feature map supervision module for classification was designed in the offline training phase to learn more discriminative embedded features.Secondly, an online target classification module that included an attention mechanism in the online tracking phase was designed, and the online update filter strategy in the module was used to filter out the background noise.Finally, an adaptive template update module was designed to update the target template information using the UpdateNet.The results of experiments on VOT2018 and VOT2019 datasets verify the effectiveness of the proposed algorithm, which brings 13.5% and 18.2% (EAO) improvement respectively compared with the baseline algorithm SiamRPN++.

Key words: machine vision, object tracking, Siamese network, object classification, adaptive template update

CLC Number:

TP391.4

Zhiwang CHEN, Zhongxin ZHANG, Juan SONG, Haipeng LEI, Yong PENG. Tracking algorithm of Siamese network based on online target classification and adaptive template update[J]. Journal on Communications, 2021, 42(8): 151-163.

Figures/Tables 11

References 24

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方案	A	R	EAO	LN/次	V_FPS/(帧.s^-1)
SiamRPN++	0.601	0.234	0.415	50	35
+ CS module	0.583	0.197	0.432	42	62

方案	A	R	EAO	LN/次	V_FPS/(帧.s^-1)
SiamRPN++	0.601	0.234	0.415	50	35
+OC module	0.611	0.150	0.417	32	28
+ OC module + CS module	0.591	0.140	0.463	30	41

方案	online update			A	R	LN/次	EAO
方案	cmp	attn	flt	A	R	LN/次	EAO
SiamRPN++	×	×	×	0.601	0.234	50	0.415
+ CS module +OC module	√	√	√	0.591	0.140	30	0.463
+CS module +OC module	×	√	√	0.589	0.150	32	0.437
+CS module +OC module	√	×	√	0.589	0.150	32	0.433
+CS module +OC module	√	√	×	0.593	0.220	47	0.406

pipeline	A	R	EAO	LN/次	V_FPS/(帧.s^-1)
SiamRPN++ +CS module	0.601	0.234	0.415	50	35
OC module +TU module	0.588	0.122	0.471	26	34

Tracker name	A	R	EAO	LN/次	V_FPS/(帧.s^-1)
SiamFC++	0.587	0.150	0.467	32	90
SiamBAN	0.590	0.178	0.447	38	40
DiMP50	0.597	0.153	0.440	33	43
SiamMargin	0.583	0.197	0.432	42	62
SiamRPN++	0.601	0.234	0.415	50	35
SiamR-CNN	0.612	0.22	0.405	47	15
ATOM	0.590	0.204	0.401	44	30
UpdateNet	0.587	0.276	0.393	59	92
DaSiamRPN	0.586	0.276	0.383	59	160
SiamMask	0.609	0.281	0.381	60	35
Ta-SiamRPN++	0.593	0.272	0.360	58	36
SiamDW	0.538	0.398	0.270	85	150
SiamFC	0.503	0.585	0.187	125	101
本文算法	0.588	0.122	0.471	26	34

Tracking algorithm of Siamese network based on online target classification and adaptive template update

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Tracker name	A	R	EAO	LN/次	V_FPS/(帧.s^-1)
SiamMargin	0.579	0.321	0.366	65	46
SiamBAN	0.602	0.396	0.327	79	40
DiMP	0.582	0.371	0.321	74	40
SiamDW_ST	0.600	0.467	0.299	93	—
SiamMask	0.594	0.461	0.287	92	35
SiamRPN++	0.599	0.482	0.285	96	35
ATOM	0.596	0.557	0.240	111	—
本文算法	0.579	0.296	0.337	59	33