Lightweight and Fast Target Tracking Algorithm Based on Ghost-TiFPN

doi:10.12382/bgxb.2022.1272

Abstract

Abstract:

In response to the problems that traditional siamese target tracking algorithm is bulky and difficult to deploy in embedded devices, and has poor effect under the conditions of large changes in target scale and similar object interference, a lightweight and fast target tracking algorithm GTtracker is proposed. The Resnet network is redesigned to build a lightweight G-Resnet network by introducing the Ghost mechanism for fast feature extraction of tracked targets. And then the fusion of feature information is further enhanced by designing a lightweight adaptive weighted fusion algorithm TiFPN to solve the problem of similar object interference. After that, a lightweight area regression network GDNet is introduced for target classification, IoU calculation, and bounding box regression, which applies a new target finder in the tracking stage to enhance the success rate of algorithm tracking. Finally, the algorithm is validated on OTB100 dataset and VOT2020 dataset, and ported to Jetson Xavier NX embedded device for performance testing. Experimental results show the effectiveness and superiority of the proposed algorithm, In comparison with classical siamese target tracking algorithm (SiamCAR), the proposed algorithm can achieve faster operation speed and real-time operation on Jetson Xavier NX, reaching 30 frames/s, under the conditions of the same accuracy and EAO metrics, which can effectively solve the problems of similar object interference and large scale variation.

Key words: target tracking, siamese network, lightweight, embedded device

CLC Number:

TP391.41

YIN Guohua, QI Yongsheng, LIU Liqiang, SU Jianqiang, ZHANG Lijie. Lightweight and Fast Target Tracking Algorithm Based on Ghost-TiFPN[J]. Acta Armamentarii, 2024, 45(5): 1703-1716.

Figures/Tables 15

References 21

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算法名称	参数量/MB	速度/(帧·s^-1)	准确率/%
ResNet50	102.15	42	86.9
G-Resnet	48.62	89	87.4

算法名称	参数量/MB	速度/(帧·s^-1)	准确率/%
ResNet50	102.15	42	86.9
G-Resnet	48.62	89	87.4

实验	基线算法	模块A	模块B	模块C	模块D	速度/(帧·s^-1)	参数量/Mb	精确度	鲁棒性	EAO
1	√	×	×	×	×	54.8	391	0.438	0.745	0.156
2	√	√	×	×	×	112.5	137	0.471	0.707	0.192
3	√	×	√	×	×	51.3	396	0.474	0.681	0.174
4	√	×	×	√	×	57	374	0.467	0.714	0.169
5	√	×	×	×	√	49	391	0.445	0.652	0.171
6	√	√	√	×	×	102	142	0.493	0.698	0.198
7	√	√	√	√	×	104	125	0.512	0.632	0.213
8	√	√	√	√	√	93	125	0.527	0.504	0.235

实验	基线算法	模块A	模块B	模块C	模块D	速度/(帧·s^-1)	参数量/Mb	精确度	鲁棒性	EAO
1	√	×	×	×	×	54.8	391	0.438	0.745	0.156
2	√	√	×	×	×	112.5	137	0.471	0.707	0.192
3	√	×	√	×	×	51.3	396	0.474	0.681	0.174
4	√	×	×	√	×	57	374	0.467	0.714	0.169
5	√	×	×	×	√	49	391	0.445	0.652	0.171
6	√	√	√	×	×	102	142	0.493	0.698	0.198
7	√	√	√	√	×	104	125	0.512	0.632	0.213
8	√	√	√	√	√	93	125	0.527	0.504	0.235

指标	算法
指标	SiamFC	CFNet	SRDCF	SiamRPN	DaSiamRPN	Ocean	SiamCAR	本文算法
EAO	0.188	0.182	0.256	0.383	0.326	0.467	0.482	0.485
精确度	0.503	0.470	0.491	0.586	0.569	0.592	0.610	0.609
鲁棒性	0.585	0.543	0.356	0.276	0.337	0.169	0.150	0.156
运行速度	72	75	8	38	59	71	51.4	86