基于校正神经网络的视频追踪算法

doi:10.6040/j.issn.1672-3961.0.2019.418

Abstract

Abstract:

In order to solve the problem that the existing deep learning based visual tracking algorithms paid attention to the deep features but neglected the shallow features, and the tracking network did not evaluate the tracking results, a visual tracking algorithm based on verifying network was proposed. The proposed algorithm consisted of tracking network and verifying network. In the tracking network, considering the fusion of deep features and shallow edge features, a multi-input residual network was designed to learn the relationship between the target and its corresponding Gaussian response map to obtain the position information of the target. In the verifying network, a shallow chain discriminate network was designed, and this paper compared the tracking results of tracking network and verifying network, and updated the tracking network according to the compared results. Therefore, the proposed algorithm not only took the deep features into account, but also avoided the loss of detail information. Furthermore, the tracking results were evaluated to prevent the continuation of error messages in the update. The experimental results illustrated that the proposed tracking algorithm achieved better tracking results than some other existing tracking methods.

Key words: visual tracking, deep neural network, verifying network, shallow feature, deep feature

CLC Number:

TP391

Ningning CHEN,Jianwei ZHAO,Zhenghua ZHOU. Visual tracking algorithm based on verifying networks[J].Journal of Shandong University(Engineering Science), 2020, 50(2): 17-26.

Figures/Tables 9

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References 35

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ω	成功率	距离精度
0	0.668	0.830
1	0.669	0.831
2	0.686	0.853
3	0.681	0.847
4	0.660	0.832

排名	IV	SV	OCC	DEF	MB	FM	IPR	OPR	OV	BC	LR
1	OUR (0.828)	OUR (0.854)	OUR (0.835)	OUR (0.851)	OUR (0.795)	OUR (0.787)	OUR (0.831)	OUR (0.844)	OUR (0.798)	OUR (0.843)	OUR (0.822)
2	CNN- SVM (0.716)	DeepS RDCF (0.750)	DeepS RDCF (0.778)	DLS- SVM (0.807)	DeepS RDCF (0.724)	DeepS RDCF (0.705)	CNN- SVM (0.754)	DeepS RDCF (0.770)	DCFNet (0.767)	RPT (0.753)	SiamFC (0.669)
3	RPT (0.713)	CNN- SVM (0.744)	DCFNet (0.767)	ACFN- selNet (0.805)	RPT (0.706)	MEEM (0.680)	STAPL E_CA (0.742)	ACFN- selNet (0.761)	ACFN- selNet (0.715)	DLS SVM (0.730)	CNN- SVM (0.612)
4	STAPL E_CA (0.710)	SiamFC (0.730)	SAMF (0.765)	CNN- SVM (0.797)	SRDCF (0.701)	RPT (0.673)	DeepS RDCF (0.730)	DLS SVM (0.758)	DeepS RDCF (0.713)	SRDCF (0.721)	DCFNet (0.590)
5	DeepS RDCF (0.703)	DCFNet (0.726)	SRDCF (0.760)	STAPL E_CA (0.767)	DLS SVM (0.686)	SiamFC (0.671)	DLS SVM (0.725)	CNN- SVM (0.756)	SiamFC (0.708)	DeepS RDCF (0.720)	DLS SVM (0.544)

排名	IV	SV	OCC	DEF	MB	FM	IPR	OPR	OV	BC	LR
1	OUR (0.673)	OUR (0.670)	OUR (0.678)	OUR (0.688)	OUR (0.664)	OUR (0.649)	OUR (0.656)	OUR (0.674)	OUR (0.708)	OUR (0.676)	OUR (0.662)
2	DCFNet (0.596)	DeepS RDCF (0.628)	DCFNet (0.645)	CNN- SVM (0.640)	DeepS RDCF (0.625)	DeepS RDCF (0.608)	STAPLE_ CA (0.601)	DeepS RDCF (0.630)	DCFNet (0.690)	RPT (0.614)	SiamFC (0.566)
3	STAPL E_CA (0.596)	DCFNet (0.619)	DeepS RDCF (0.628)	SRDCF (0.635)	SRDCF (0.601)	SRDCF (0.569)	DeepS RDCF (0.596)	DCFNet (0.612)	SiamFC (0.635)	CNN- SVM (0.593)	DCFNet (0.496)
4	DeepS RDCF (0.589)	SiamFC (0.603)	SRDCF (0.627)	STAPL E_CA (0.632)	DLS SVM (0.578)	STAPL E_CA (0.566)	SiamFC (0.582)	SRDCF (0.599)	DeepS RDCF (0.619)	DLS SVM (0.592)	CNN- SVM (0.461)
5	SRDCF (0.576)	SRDCF (0.587)	SAMF (0.612)	DLS SVM (0.632)	RPT (0.576)	RPT (0.561)	STAPLE (0.580)	STAPL E_CA (0.594)	ACFN- selNet (0.609)	DeepS RDCF (0.591)	STAPLE (0.438)

追踪器	距离精度						成功率
追踪器	综合	CM	IC	MC	OCC	SC	综合	CM	IC	MC	OCC	SC
OUR	0.668	0.663	0.663	0.663	0.663	0.668	0.502	0.500	0.500	0.500	0.492	0.502
DeepSTRCF	0.595	0.589	0.589	0.589	0.585	0.595	0.460	0.454	0.454	0.454	0.447	0.460
CFWCR	0.586	0.579	0.579	0.579	0.576	0.586	0.453	0.448	0.448	0.448	0.444	0.453
STRCF	0.548	0.541	0.541	0.541	0.538	0.548	0.418	0.412	0.412	0.412	0.405	0.418
CREST	0.526	0.519	0.519	0.519	0.519	0.526	0.423	0.418	0.418	0.418	0.412	0.423
BACF	0.464	0.456	0.456	0.456	0.448	0.464	0.366	0.360	0.360	0.360	0.351	0.366
DCFNet	0.454	0.445	0.445	0.445	0.437	0.454	0.367	0.360	0.360	0.360	0.351	0.367
MRNet	0.448	0.440	0.440	0.440	0.437	0.448	0.360	0.354	0.354	0.354	0.346	0.360
SAMF	0.427	0.423	0.423	0.423	0.407	0.427	0.337	0.335	0.335	0.335	0.322	0.337
SRDCF	0.419	0.410	0.410	0.410	0.402	0.419	0.325	0.317	0.317	0.317	0.311	0.325
KCF	0.367	0.358	0.358	0.358	0.355	0.367	0.276	0.270	0.270	0.270	0.265	0.276
CSK	0.318	0.308	0.308	0.308	0.318	0.318	0.241	0.233	0.233	0.233	0.238	0.241
DSST	0.294	0.283	0.283	0.283	0.305	0.294	0.219	0.210	0.210	0.210	0.227	0.219
DFT	0.260	0.254	0.254	0.254	0.264	0.260	0.213	0.208	0.208	0.208	0.212	0.213
CT	0.233	0.234	0.234	0.234	0.239	0.233	0.195	0.197	0.197	0.197	0.195	0.195

Visual tracking algorithm based on verifying networks

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