Target Scale Adaptive Robust Tracking Based on Fusion of Multilayer Convolutional Features

Xin Wang; Zhiqiang Hou; Wangsheng Yu; Zefenfen Jin; Xianxiang Qin

doi:10.3788/AOS201737.1115005

Acta Optica Sinica, Volume. 37, Issue 11, 1115005(2017)

Target Scale Adaptive Robust Tracking Based on Fusion of Multilayer Convolutional Features

Xin Wang^*, Zhiqiang Hou, Wangsheng Yu, Zefenfen Jin, and Xianxiang Qin

Author Affiliations

Information and Navigation College, Air Force Engineering University, Xi'an, Shaanxi 710077, China

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Figures & Tables(14)

Fig. 1. Schematic of deep convolution network of VGG-Net-19

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Fig. 2. Visualizations for different convolutional layers of VGG-Net-19. (a) Input images; (b) Conv3-4; (c) Conv4-4; (d) Conv5-4

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Fig. 3. Construct the scale pyramid of the target by multi-scale sampling

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Fig. 4. Flow chart of proposed algorithm

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Fig. 5. Comparison of partial tracking results of seven trackers

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Fig. 6. Center location error curves of eight test sequences

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Fig. 7. Overlap rate curves of eight test sequences

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Fig. 8. (a) Success rate curves and (b) precision curves of 28 test sequences

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Fig. 9. Tracking performance analysis in different combinations of feature. (a) Success rate curves; (b) precision curves

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Table 1. Scale adaptive robust tracker based on fusion of multilayer convolutional features

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Table 1. Scale adaptive robust tracker based on fusion of multilayer convolutional features

Input: Image sequence: I₁, I₂, …, I_n. Initial target position: p₀=(x₀, y₀), and initial target scale: s₀=(w₀, h₀).
Output: The estimated position of target: p_t=(x_t, y_t), and estimated scale: s_t=(w_t, h_t).
For t=1,2,…,n, do:
1	Locate the Center of Target
1.1	Crop out the ROI image in frame #t centered at p_t_-1, and extract the hierarchical convolutional features;
1.2	Learn the correlation response map using Eq. (5) and Eq. (7) for each convolutional layer;
1.3	Fuse the multiple correlation response maps using Eq. (8), and obtain the compositive response map;
1.4	Locate the center of the target p_t in frame #t using Eq. (9).
2	Estimate the Scale of Target
2.1	Obtain the multi-scale sample images I_s={I_s₁,…, I_sm} in frame #t based on p_t and s_t_-1;
2.2	Build scale filters by extracting HOG features from the above multi-scale sample images;
2.3	Compute the correlation response score using Eq. (10) and Eq. (11);
2.4	Estimate the optimal scale s_t of the target in frame #t using Eq. (12).
3	Model Update
3.1	Update the position filters using Eq. (13);
3.2	Update the scale filters using Eq. (14).
Until End of the image sequence.

Table 2. Comparison of the tracking precisions of the algorithm of different attributes

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Table 2. Comparison of the tracking precisions of the algorithm of different attributes

Algorithm	SV(28)	IV(15)	OCC(16)	BC(11)	DEF(9)	MB(8)	FM(12)	IPR(18)	OPR(23)	OV(4)	LR(3)
Proposed	0.880	0.838	$\begin{matrix} \bar{0.841} \end{matrix}$	$\begin{matrix} \bar{0.861} \end{matrix}$	0.932	0.870	0.772	0.879	$\begin{matrix} \bar{0.855} \end{matrix}$	0.702	0.873
HCF	0.880	0.858	0.847	0.867	$\begin{matrix} \bar{0.927} \end{matrix}$	$\begin{matrix} \bar{0.844} \end{matrix}$	$\begin{matrix} \bar{0.757} \end{matrix}$	$\begin{matrix} \bar{0.873} \end{matrix}$	0.857	0.656	$\begin{matrix} \bar{0.863} \end{matrix}$
FCNT	$\begin{matrix} \bar{0.830} \end{matrix}$	0.779	0.737	0.713	0.925	0.740	0.715	0.774	0.798	$\begin{matrix} \bar{0.691} \end{matrix}$	0.686
CNN-SVM	0.827	0.751	0.733	0.689	0.890	0.725	0.685	0.793	0.800	0.650	0.606
CNT	0.662	0.521	0.667	0.463	0.686	0.479	0.477	0.583	0.630	0.481	0.410
DSST	0.740	0.681	0.785	0.610	0.733	0.635	0.539	0.714	0.725	0.453	0.402
KCF	0.680	0.632	0.744	0.578	0.734	0.679	0.586	0.619	0.678	0.639	0.233

Table 3. Comparison of the tracking success rates of the algorithm of different attributes

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Table 3. Comparison of the tracking success rates of the algorithm of different attributes

Algorithm	SV(28)	IV(15)	OCC(16)	BC(11)	DEF(9)	MB(8)	FM(12)	IPR(18)	OPR(23)	OV(4)	LR(3)
Proposed	0.600	0.556	0.582	0.586	0.629	$\begin{matrix} \bar{0.591} \end{matrix}$	0.554	0.591	0.579	0.527	0.574
HCF	0.531	0.509	0.514	$\begin{matrix} \bar{0.573} \end{matrix}$	0.589	0.594	$\begin{matrix} \bar{0.545} \end{matrix}$	$\begin{matrix} \bar{0.532} \end{matrix}$	0.525	0.522	$\begin{matrix} \bar{0.497} \end{matrix}$
FCNT	$\begin{matrix} \bar{0.558} \end{matrix}$	$\begin{matrix} \bar{0.551} \end{matrix}$	$\begin{matrix} \bar{0.517} \end{matrix}$	0.506	$\begin{matrix} \bar{0.628} \end{matrix}$	0.552	0.533	0.504	$\begin{matrix} \bar{0.539} \end{matrix}$	0.573	0.451
CNN-SVM	0.513	0.477	0.473	0.500	0.594	0.535	0.513	0.480	0.504	$\begin{matrix} \bar{0.536} \end{matrix}$	0.373
CNT	0.508	0.425	0.506	0.372	0.541	0.426	0.411	0.442	0.475	0.417	0.342
DSST	0.451	0.412	0.462	0.421	0.491	0.457	0.411	0.441	0.446	0.405	0.238
KCF	0.427	0.389	0.458	0.398	0.501	0.512	0.450	0.383	0.425	0.520	0.209

Table 4. Tracking speed of proposed algorithm for the eight videosframe /s
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Table 4. Tracking speed of proposed algorithm for the eight videosframe /s
Video CarScale Dog1 Doll Ironman MotorRolling Skiing Soccer Walking2 Average
Tracking speed 9.0 8.3 9.7 6.7 3.1 12.1 4.7 9.6 7.9

Table 5. Comparison of average tracking speed of the trackers based on deep learningframe /s
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Table 5. Comparison of average tracking speed of the trackers based on deep learningframe /s
Tracker Proposed CNT FCNT CNN-SVM HCF MDNet DeepTrack^[29] STCT^[30]
Code M+C M M C+M M+C M M C+M
Platform CPU+GPU CPU CPU+GPU CPU+GPU GPU CPU+GPU CPU+GPU CPU+GPU
Average tracking speed 8.5 5 3 - 10 1 2.5 2.5

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Xin Wang, Zhiqiang Hou, Wangsheng Yu, Zefenfen Jin, Xianxiang Qin. Target Scale Adaptive Robust Tracking Based on Fusion of Multilayer Convolutional Features[J]. Acta Optica Sinica, 2017, 37(11): 1115005

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Paper Information

Category: Machine Vision

Received: Jun. 21, 2017

Accepted: --

Published Online: Sep. 7, 2018

The Author Email: Xin Wang (wangxiin@foxmail.com)

DOI:10.3788/AOS201737.1115005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Scale adaptive robust tracker based on fusion of multilayer convolutional features

Table 1. Scale adaptive robust tracker based on fusion of multilayer convolutional features

Table 2. Comparison of the tracking precisions of the algorithm of different attributes

Table 2. Comparison of the tracking precisions of the algorithm of different attributes

Table 3. Comparison of the tracking success rates of the algorithm of different attributes

Table 3. Comparison of the tracking success rates of the algorithm of different attributes

Table 4. Tracking speed of proposed algorithm for the eight videosframe /s

Table 4. Tracking speed of proposed algorithm for the eight videosframe /s

Table 5. Comparison of average tracking speed of the trackers based on deep learningframe /s

Table 5. Comparison of average tracking speed of the trackers based on deep learningframe /s