Object Tracking Algorithm Based on Correlation Filtering and Convolution Residuals Learning

Yaguang Yang; Zhenhong Shang

doi:10.3788/LOP57.121012

Laser & Optoelectronics Progress, Volume. 57, Issue 12, 121012(2020)

Object Tracking Algorithm Based on Correlation Filtering and Convolution Residuals Learning

Yaguang Yang^** and Zhenhong Shang^*

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, China

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Figures & Tables(8)

Fig. 1. Network frame diagram of proposed algorithm

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Fig. 2. Basic convolution layer and temporal-spatial residual layer

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Fig. 3. Comparison experiment on OTB2015 datasets with baseline. (a) Precision; (b) success rate

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Fig. 4. Distance precision plots and overlap success plots of ten algorithms in OTB-2013. (a) Precision; (b) success rate

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Fig. 5. Distance precision plots and overlap success plots of ten algorithms in OTB-2015. (a) Precision; (b) success rate

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Fig. 6. A visualization of the tracking results of seven algorithms on eight sequences

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Table 1. DP values of proposed method and compared methods under different scene attributes

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Table 1. DP values of proposed method and compared methods under different scene attributes

Item	Proposed	CF2	Scale_DLSSVM	DeepSRDCF	SiamFC	Staple	SAMF	KCF	DSST	Struck
IV	0.862	$\begin{matrix} \bar{0.816} \end{matrix}$	0.790	0.786	0.741	0.787	0.708	0.724	0.715	0.558
SV	0.872	0.798	0.758	$\begin{matrix} \bar{0.817} \end{matrix}$	0.738	0.723	0.701	0.635	0.633	0.595
OCC	0.893	0.765	0.789	$\begin{matrix} \bar{0.822} \end{matrix}$	0.726	0.721	0.722	0.632	0.590	0.528
DEF	0.849	$\begin{matrix} \bar{0.790} \end{matrix}$	0.748	0.779	0.693	0.743	0.680	0.619	0.533	0.527
MB	0.883	0.804	0.740	$\begin{matrix} \bar{0.823} \end{matrix}$	0.705	0.707	0.655	0.601	0.567	0.580
FM	0.871	$\begin{matrix} \bar{0.815} \end{matrix}$	0.727	0.814	0.743	0.697	0.654	0.621	0.552	0.606
IPR	0.860	$\begin{matrix} \bar{0.854} \end{matrix}$	0.820	0.818	0.742	0.770	0.721	0.701	0.691	0.629
OPR	0.886	$\begin{matrix} \bar{0.807} \end{matrix}$	0.802	0.835	0.756	0.738	0.739	0.677	0.644	0.587
OV	0.864	0.677	0.704	$\begin{matrix} \bar{0.781} \end{matrix}$	0.669	0.661	0.628	0.501	0.481	0.472
BC	0.866	$\begin{matrix} \bar{0.843} \end{matrix}$	0.793	0.841	0.690	0.766	0.689	0.713	0.704	0.552
LR	0.872	0.831	0.791	0.708	$\begin{matrix} \bar{0.847} \end{matrix}$	0.631	0.685	0.560	0.567	0.671
Note: In this table, the number marked with black is the first, and the number underlined is the second.

Table 2. OP values of proposed method and compared methods under different scene attributes

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Table 2. OP values of proposed method and compared methods under different scene attributes

Item	Proposed	CF2	Scale_DLSSVM	DeepSRDCF	SiamFC	Staple	SAMF	KCF	DSST	Struck
IV	0.677	0.541	0.572	$\begin{matrix} \bar{0.624} \end{matrix}$	0.574	0.596	0.530	0.482	0.556	0.428
SV	0.652	0.485	0.500	$\begin{matrix} \bar{0.607} \end{matrix}$	0.556	0.522	0.492	0.395	0.466	0.403
OCC	0.672	0.526	0.551	$\begin{matrix} \bar{0.603} \end{matrix}$	0.547	0.545	0.538	0.445	0.449	0.393
DEF	0.615	0.530	0.522	$\begin{matrix} \bar{0.567} \end{matrix}$	0.510	0.552	0.505	0.438	0.415	0.387
MB	0.701	0.585	0.591	$\begin{matrix} \bar{0.642} \end{matrix}$	0.550	0.546	0.525	0.459	0.469	0.459
FM	0.673	0.570	0.550	$\begin{matrix} \bar{0.628} \end{matrix}$	0.568	0.537	0.507	0.459	0.447	0.462
IPR	0.622	0.559	0.564	$\begin{matrix} \bar{0.589} \end{matrix}$	0.557	0.552	0.519	0.469	0.502	0.448
OPR	0.648	0.534	0.547	$\begin{matrix} \bar{0.607} \end{matrix}$	0.558	0.534	0.536	0.453	0.470	0.423
OV	0.639	0.474	0.498	$\begin{matrix} \bar{0.553} \end{matrix}$	0.506	0.481	0.480	0.393	0.386	0.365
BC	0.645	0.585	0.560	$\begin{matrix} \bar{0.627} \end{matrix}$	0.523	0.574	0.525	0.498	0.523	0.429
LR	0.597	0.439	0.433	0.475	$\begin{matrix} \bar{0.592} \end{matrix}$	0.418	0.430	0.307	0.383	0.363
Note: In this table, the number marked with black is the first, and the number underlined is the second.

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Yaguang Yang, Zhenhong Shang. Object Tracking Algorithm Based on Correlation Filtering and Convolution Residuals Learning[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121012

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Paper Information

Category: Image Processing

Received: Sep. 2, 2019

Accepted: Nov. 2, 2019

Published Online: Jun. 3, 2020

The Author Email: Yang Yaguang (1107298031@qq.com), Shang Zhenhong (shangzhenhong@126.com)

DOI:10.3788/LOP57.121012

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology