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1 4: Optical Flow CCTV (Jihae Kim et al.: Optical Flow Based Vehicle Counting and Speed Estimation in CCTV Videos) (Regular Paper) 22 4, (JBE Vol. 22, o. 4, July 2017) ISS (Online) ISS (Print) Optical Flow CCTV a), a), a), a), b) Optical Flow Based Vehicle Counting and Speed Estimation in CCTV Videos Jihae Kim a), okyung Shin a), Jaekyung Kim a), Cheolhee Kwon a), and Hyeran Byun b) CCTV. (IPM, Inverse Perspective Mapping), 1), 2). (Optical flow). CCTV 106,993, 88.94%. Abstract This paper proposes a vehicle counting and speed estimation method for traffic situation analysis in road CCTV videos. The proposed method removes a distortion in the images using Inverse perspective Mapping, and obtains specific region for vehicle counting and speed estimation using lane detection algorithm. Then, we can obtain vehicle counting and speed estimation results from using optical flow at specific region. The proposed method achieves stable accuracy of 88.94% from several CCTV images by regional groups and it totally applied at 106,993 frames, about 3 hours video. Keyword : Vehicle Counting, Vehicle Speed Estimation, Inverse Perspective Mapping, Optical Flow, Traffic Situation Analysis a) LIG.Project 5(LIG ex1 Avionics R& Lab) b) (Yonsei University, ept. Computer Science) Corresponding Author : (Hyeran Byun) hrbyun@yonsei.ac.kr Tel: ORCI: () (o ,eep Learning ) Manuscript received April 3, 2017; Revised July 7, 2017; Accepted July 7, Copyright 2017 Korean Institute of Broadcast and Media Engineers. All rights reserved. This is an Open-Access article distributed under the terms of the Creative Commons BY-C- ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and not altered.
2 (JBE Vol. 22, o. 4, July 2017). 10 (ITS, Intelligent Transportation System) [1]-[3],. ITS.. [4],[5],[6],[7],[8].,..... [4]-[6],[9]... [4],[5]. (OF, Optical Flow), (F, Frame ifference), (BS, Background Subtraction)., - [10] (Vision) [6],[7],[9],[11],[12].. CCTV (IPM, Inverse Perspective Mapping) [13] -(Top-view) Fig. 1. The flow of the proposed method. 2, 3 IPM.. 4, 5.
3 4: Optical Flow CCTV (Jihae Kim et al.: Optical Flow Based Vehicle Counting and Speed Estimation in CCTV Videos)..,. 1.. (F, Frame ifference), (BS, Background Subtraction), (OF, Optical Flow) [4],[5],[14]-[16]. (F, Frame ifference) [4],[5].,,. (BS, Background Subtraction) [14],[16].. (background modeling).,,. [14],[16] CCTV. (OF, Optical Flow) [15],[17].. 2. /, -, - [6],[7],[9],[11],[12],[18].. Inertial avigation System(IS) (wheel speed sensor)..,. height-precision GPS. GPS,. - [18] kinematic dynamics model... - [6],[7],[9],[11],[12]. [7],[11]. [12] Lucas- Kanade Horn-Schunck.. CCTV CCTV. CCTV
![2 (resampling). 2 (Euclidean). (), ( ) ( ), (), () (2) 2 3. CCTV... (IPM, Inverse Perspective Mapping) [13] -.](/docs-images/92/110652170/images/4-1.jpg "tan cos tan sin (a) Input image (b) Vanishing point detection (c) Result of IPM 2. Fig. 2. Examples of applying IPM")
4 (JBE Vol. 22, o. 4, July 2017) 1) (IPM, Inverse Perspective Mapping), 2), 3) (OF, Optical Flow), 4) 4. (perspective distortion) IPM -,.., IPM IPM (IPM, Inverse Perspective Mapping). 2, 3. [19]. 2 (resampling). 2 (Euclidean). (), ( ) ( ), (), () (2) 2 3. CCTV... (IPM, Inverse Perspective Mapping) [13] -. tan cos tan sin (a) Input image (b) Vanishing point detection (c) Result of IPM 2. Fig. 2. Examples of applying IPM
![, IPM. 2. IPM -. [13],[20] 3. 3(a) 3.1 IPM -. -., -(top-hat). 3(b), 3(c).](/docs-images/92/110652170/images/5-1.jpg "[21] (structuring element),. 3(d). (column), 5 (bin). 3(e) - 3(f). 2/3, 20. 3(g). -. (3) n-1, n ( ) 0.")
5 4: Optical Flow CCTV (Jihae Kim et al.: Optical Flow Based Vehicle Counting and Speed Estimation in CCTV Videos) 2 IPM., IPM. 2. IPM -. [13],[20] 3. 3(a) 3.1 IPM -. -., -(top-hat). 3(b), 3(c). [21] (structuring element),. 3(d). (column), 5 (bin). 3(e) - 3(f). 2/3, 20. 3(g). -. (3) n-1, n ( ) 0.6,. 3. Fig. 3 Flow of lane detection algorithm
6 (JBE Vol. 22, o. 4, July 2017) 3. CCTV. IPM. Farneback (dense optical flow)..,. (sparse optical flow), (dense optical flow). Lucas-kanade,.. Farneback. Farneback. 4 step. 3.2,. 4. Fig. 4. Example of motion measuring regionounting Fig. 5 The flow of vehicle counting
7 4: Optical Flow CCTV (Jihae Kim et al.: Optical Flow Based Vehicle Counting and Speed Estimation in CCTV Videos). (maxflow) (4). max max max,, ( ) ( ) (5), (6).,.. 4. CCTV. Lucas-Kanade. 3.2 Lucas-Kanade.., Lucas-Kanade,.... ( ) 1000cm(10m). (7).,,,. (7). (8)., (7), FPS C km/h. (8). 1.. CCTV.
8 (JBE Vol. 22, o. 4, July 2017). 1. 2, 3, 4. (ay) (ight) 16., 106, Table 1. Test set composition for experiment CCTV ay ight total Busan 7,000 6,218 13,218 Goyang 7,000 7,000 14,000 Gimpo1 7,000 7,000 14,000 Gimpo2 5,575 6,787 12,362 Yangsan 7,000 7,000 14,000 Ceongdo 7,000 7,000 14,000 Suseong 5,873 5,813 11,686 amyangju 6,727 7,000 13,727 total 53,175 53, , Table 2. Composition of testset for evaluation # lane CCTV 4 G Busan_ Busan_ oyang_ Goyang_ 3 Gimpo1_ Gimpo2_ Yangsan_ Gimpo1_ Gimpo2_ Yangsan_ 2 Ceongdo_ Suseong_ amyangju_ Ceongdo_ Suseong_ amyangju_
9 4: Optical Flow CCTV (Jihae Kim et al.: Optical Flow Based Vehicle Counting and Speed Estimation in CCTV Videos) 2.. ground truth. (9) (10)., G ground truth, C. (9) AE (10) ground truth(g), experimental result(c),,. 3 4, 4 5 3, ground truth(g), experimental result(c), % %, 87.32%.. 7 (background subtraction) [14], (optical flow) Lucas-kanade.,,, 56.94%, 63.57%, 88.94%.. Gimpo Table 3. Experiment result of 4 lanes of video CCTV Lane 1 Lane 2 Lane 3 Lane 4 total ground truth experimental result accuracy(%) Busan average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) Goyang average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h)
10 (JBE Vol. 22, o. 4, July 2017) 4. 3 Table 4. Experiment result of 3 lanes of video CCTV Lane 1 Lane 2 Lane 3 total Gimpo1 Gimpo2 Yangsan ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) Table 5. Experiment result of 2 lanes of video CCTV Lane 1 Lane 2 total ground truth experimental result accuracy(%) Ceongdo average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) Suseong average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h) ground truth experimental result accuracy(%) amyangju average speed(km/h) ground truth experimental result accuracy(%) average speed(km/h)
11 4: Optical Flow CCTV (Jihae Kim et al.: Optical Flow Based Vehicle Counting and Speed Estimation in CCTV Videos) 6. Table 6. Accuracy of vehicle counting on day and night video Accuracy(%) ay ight total Fig. 6. Result of vehicle counting experiment 7. Fig. 7. Result of vehicle counting with comparative experiment
12 (JBE Vol. 22, o. 4, July 2017) 8. Fig. 8. Result of vehicle speed estimation... Farneback, Lucas-kanade.. Farneback. 8.,... CCTV. (IPM, Inverse Perspective Mapping),..,, CCTV..,
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