通道裁剪下的多特征组合目标跟踪算法

doi:10.3969/j.issn.1001-506X.2020.04.05

Abstract

Abstract:

Aiming to solve the problems that the redundant channel in depth features affects the tracking speed and accuracy, and the single feature cannot adapt to all complex scenes, a multi-feature combined target tracking algorithm based on channel clipping is proposed. Firstly, the traditional manual features and depth features are combined for tracking. Secondly, a channel clipping strategy is designed by comparing the deep feature means of each channel in the target and searching areas. Then, the depth feature is updated by the proposed interval updating strategy, and the traditional feature filter template is updated by the average peak correlation energy. Compared with the 10 algorithms on the OTB2013 and OTB2015 datasets, the results show that the proposed algorithm achieves better results in tracking accuracy and success rate.

Key words: object tracking, channel clipping, correlation filter, feature combination

CLC Number:

TP391

Yu XIE, Ying CHEN. Multi-feature combined target tracking algorithm based on channel clipping[J]. Systems Engineering and Electronics, 2020, 42(4): 764-772.

Figures/Tables 14

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References 14

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算法名称	OTB2013			OTB2015			fps
算法名称	DP/%	OS/%	CLE/像素	DP/%	OS/%	CLE/像素	fps
FCCT	87.5	79.7	16.7	90.8	85.3	12.2	14.5
TADT^[10]	83.6	76.9	18.2	86.4	81.3	16.3	33.7
ADNet^[13]	83.4	74.6	20.9	87.9	80.8	14.8	2.9
LSCT^[12]	76.8	66.5	34.3	83.0	74.6	24.0	13.8
HCFTs^[16]	84.2	68.9	22.8	87.5	72.7	16.2	0.7
MCPF^[11]	84.9	72.0	24.9	87.5	78.3	20.7	0.5
HDT^[14]	80.9	60.2	24.7	84.9	66.3	20.0	5.5
CACF^[15]	73.4	64.9	41.8	80.8	72.8	31.8	38.5
HCF^[4]	81.0	69.7	27.8	83.1	74.2	24.8	10.3
LCT^[17]	70.1	63.5	103.2	76.5	70.6	66.5	20.7
KCF^[6]	61.5	47.3	53.6	69.5	55.6	44.8	217.7

参数	FCCT	ADNet	TADT	HCFTs	LSCT	MCPF	HDT	HCF	CACF	KCF	LCT
光照变化	91.1	90.3	86.1	90.2	84.3	88.6	82.3	84.3	82.5	71.8	75.4
平面外旋转	90.7	87.8	87.2	84.9	82.6	86.7	80.5	80.8	76.4	67.7	74.6
尺度变化	88.3	86.6	86.3	83.6	79.1	86.7	81.3	80.1	76.2	63.7	69.0
遮挡	85.9	83.9	84.5	81.7	76.7	86.5	77.8	77.8	74.5	63.7	68.8
形变	87.2	84.8	81.4	81.8	77.4	80.7	81.3	75.8	76.7	59.9	68.0
运动模糊	88.4	85.3	83.9	82.8	79.0	84.5	79.6	80.7	75.7	61.4	68.0
平面内旋转	92.0	87.1	83.5	89.7	85.1	89.0	84.7	86.8	80.0	70.7	78.1
出视野	83.1	80.2	81.6	74.6	67.1	76.4	66.3	66.2	66.6	50.1	59.2
背景混杂	91.4	90.9	79.9	90.3	84.8	82.7	84.8	82.4	79.2	71.2	74.4
快速运动	87.7	83.3	81.9	80.9	79.5	83.0	80.4	79.4	74.7	61.7	67.3
低分辨率	82.7	85.1	87.7	88.1	77.0	94.1	77.2	83.2	56.3	52.4	49.6

参数	FCCT	ADNet	TADT	HCFTs	LSCT	MCPF	HDT	HCF	CACF	KCF	LCT
光照变化	87.6	84.5	82.6	74.7	76.9	76.7	62.3	78.9	73.8	56.2	73.1
平面外旋转	84.0	77.7	80.6	68.5	72.5	76.7	62.7	70.6	67.2	52.7	67.6
尺度变化	80.2	78.8	79.9	60.7	66.9	73.8	52.9	67.2	64.8	43.1	59.9
遮挡	81.8	75.7	81.0	67.3	71.6	75.8	61.9	71.2	69.1	52.2	64.1
形变	79.8	75.8	74.0	66.6	69.4	69.5	62.1	66.6	69.4	50.0	61.8
运动模糊	82.9	83.8	83.3	75.0	73.6	72.5	70.0	76.5	71.7	56.5	67.0
平面内旋转	81.7	76.1	76.0	71.2	70.6	77.0	64.7	71.7	68.9	54.5	68.7
出视野	70.5	72.3	74.5	61.8	61.9	62.9	54.7	58.4	57.6	45.7	53.1
背景混杂	86.4	84.5	76.6	80.4	76.6	76.1	73.5	76.8	72.3	62.7	72.0
快速运动	81.5	78.5	78.2	69.2	72.1	70.8	65.8	73.1	69.3	52.7	64.9
低分辨率	68.5	74.1	79.5	64.0	61.6	72.3	46.1	65.2	45.9	32.9	42.3

通道衡量条件	具体含义
feature_cut_1	中位数的时域均值
feature_cut_2	中位数的时域中位数
feature_cut_3	中位数的时域最大值
feature_cut_4	中位数的时域最小值
feature_cut_5	阈值1
feature_cut_6	阈值1.5

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Multi-feature combined target tracking algorithm based on channel clipping

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