Review on Object Detection and Recognition in Large Field of View

Tangwei Li; Guanjun Tong; Baoqing Li; Xiaoyang Lu

doi:10.3788/LOP57.120002

Laser & Optoelectronics Progress, Volume. 57, Issue 12, 120002(2020)

Review on Object Detection and Recognition in Large Field of View

Tangwei Li^1,2, Guanjun Tong^1、*, Baoqing Li¹, and Xiaoyang Lu¹

¹Key Laboratory of Science and Technology on Micro-System, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China

²University of Chinese Academy of Sciences, Beijing 100049, China

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Figures & Tables(12)

Fig. 1. Inconsistent distortion of object

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Fig. 2. Incomplete and blurring object in LFOV images

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Fig. 3. Distortion of small object in LFOV images

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Fig. 4. Asymmetry of object in different LFOV images

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Fig. 5. Classification of object detection and recognition in large field of view

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Fig. 6. Flow chart of object detection and recognition based on distortion correction

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Fig. 7. Road map of object detection and recognition in large field of view

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Table 1. Comparison of performance of different algorithms

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Table 1. Comparison of performance of different algorithms

Paper	Object	Pre-process	Feature extraction	Evaluation	Year
Jeong et al.^[22]	Vehicle	Undistortion using FOV $\begin{matrix} r_{u} = \frac{\tan (r_{d} w)}{2 \tan (\frac{w}{2})} \end{matrix}$	HOG		2016
Silbersteinet al.^[24]	Pedestrian	Camera calibration	AFS	10.3%(avgMRF)	2014
Levi andSilberstein^[25]	Pedestrian	Camera calibration	AFS-Multi-Cue	3.5%(avgMRF)	2015
Bertozziet al.^[26]	Pedestrian	Image un-warping $\begin{matrix} [\begin{matrix} u \\ v \end{matrix}] = [\begin{matrix} c_{u} + atan 2 (x, z) \cdot f_{θ} \\ c_{v} + k_{v} \cdot \frac{y}{\sqrt[]{x^{2} + y^{2}}} \end{matrix}] \end{matrix}$	Soft-Cascadeand ACF	0.35(FPPI is 10^-1),0.59 (FPPI is 10^-2),0.75 (FPPI is 10^-3)	2015
Suhr et al.^[28]	Pedestrian	Image warping $\begin{matrix} \begin{matrix} θ = \arctan (x / f) \\ ϕ = \arctan (y / \sqrt[]{x^{2} + y^{2}}) \\ x' = sθ \\ y' = sln (secϕ + tanϕ) \end{matrix} \end{matrix}$	HOG andTER-based classifier	97.3%(TWA)	2017

Table 2. Comparison of performance of different algorithms

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Table 2. Comparison of performance of different algorithms

Paper	Object	Pre-process	Feature extraction	Evaluation	Year
Martinezet al.^[31]	Humanbeings	Transformation $\begin{matrix} \begin{matrix} ϕ = c / R_{med} \\ R_{med} = (R_{\min} + R_{\max} \frac{)}{2.0} \\ c_{0} = c_{1} + (R_{\min} + r) sinϕ \\ r_{0} = r_{1} + (R_{\min} + r) cosϕ \end{matrix} \end{matrix}$	Viola-Jonesclassifier	Time reducedfrom600-700 ms to10-15 ms	2010
Dinget al.^[32]	Motionobject	$\begin{matrix} \begin{matrix} \bar{x} = x + {k_{1} x (x^{2} + y^{2}) + s_{1} (x^{2} + y^{2}) + \\ [p_{1} (3 x^{2} + y^{2}) + 2 p_{1} xy]} \\ \bar{y} = y + {k_{2} x (x^{2} + y^{2}) + s_{2} (x^{2} + y^{2}) + \\ [p_{2} (3 x^{2} + y^{2}) + 2 p_{2} xy]} \end{matrix} \end{matrix}$	Based ontexture andcolor feature	5× faster	2016

Table 3. Comparison of performance of different algorithms

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Table 3. Comparison of performance of different algorithms

Paper	Object	Pre-process	Feature extraction	Evaluation	Date
Yoshimiet al.^[36]	Pedestrian	HPM	Faster R-CNN	LAMR: 24.5%;E=48.65%	2017
Cai et al.^[37]	VOC Pascal	Cylindrical unwarpingCorrection	YOLO	Detection rate: 30.89 frame·s^-1Accuracy rate: 72%	2018
Xu et al.^[38]	Face	Spherical projection	LCNN	97.3%(TWA)	2018
Deng et al.^[40]	Fire	Spherical projection	CNN		2017

Table 4. Comparison of performance of different algorithms

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Table 4. Comparison of performance of different algorithms

Method	Advantage	Disadvantage
Camera calibration	Higher precision	Complexity
	No restrictions on camera type	Need to know a certain size of calibration object
Non-metric calibration	Simple	Worse stability
	Few parameters	Not suitable for high-precision system

Table 5. Comparison of performance of different algorithms

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Table 5. Comparison of performance of different algorithms

Paper	Object	Method	Evaluation	Year
Deng et al.^[42]	20 class	Faster R-CNN	mAP: 68.7%	2017
Coors et al.^[49]	Flying cars	Encode the invariance of geometrictransformation directly into CNN	mAP: 50.18%	2018
Herceg et al.^[62]	Motion object	Corner detection, optical flow	Accuracy: 97.19%	2011
Wu et al.^[65]	Motion object	Moving blob method; PTZ shots image	Accuracy: 92%	2017
Zhang et al.^[67]	Salient object	Co-saliency detection algorithm	Precision: 0.82;recall: 0.75; F₁: 0.81	2017
Cinaroglu et al.^[69]	People	Omnidirectional sliding windowModified HOG+SVM	SVM scores: 2.94	2014
Wang et al.^[70]	People	Template-based	MOTA: 0.85	2017

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Tangwei Li, Guanjun Tong, Baoqing Li, Xiaoyang Lu. Review on Object Detection and Recognition in Large Field of View[J]. Laser & Optoelectronics Progress, 2020, 57(12): 120002

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Paper Information

Category: Reviews

Received: Jul. 19, 2019

Accepted: Oct. 31, 2019

Published Online: Jun. 3, 2020

The Author Email: Tong Guanjun (tongguanjun@mail.sim.ac.cn)

DOI:10.3788/LOP57.120002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology