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http://dx.doi.org/10.6109/jkiice.2021.25.12.1790

Building Method an Image Dataset for Tracking Objects in a Video  

Kim, Ji-Seong (Department of Computer Engineering, Dong-Eui University)
Heo, Gyeongyong (Department of Electronic Engineering, Dong-eui University)
Jang, Si-Woong (Department of Computer Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1790-1796 More about this Journal
Abstract
A large amount of image data sets are required for image deep learning, and there are many differences in the method of obtaining images and constructing image data sets depending on the type of object. In this paper, we presented a method of constructing an image data set for deep learning and analyzed the performance that varies depending on the object to be tracked. We took a video by rotating the object, and then created a data set by segmenting the video using the proposed data set construction method. As a result of performance analysis, detection rate was more than 95%, and detection rate of objects with little change in shape was higher performance. It is considered that it is effective to use the data set construction method presented in this paper for a situation in which it is difficult to obtain image data and to track an object with little change in shape within a video.
Keywords
Artificial intelligence; Deep learning; Datasets; Object tracking;
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1 T. J. Lee, Y. J. Kim, and H. K. Jung, "Dataset Construction and Model Learning for Manufacturing Worker Safety Management," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 7, pp. 890-895, Jul. 2021.   DOI
2 J. W. Lee, D. J. Lee, S. J. Jang, D. G. Choi, and J. W. Jang, "Analysis of Deep Learning Model for the Development of an Optimized Vehicle Occupancy Detection System," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 1, pp. 146-151, Jan. 2021.   DOI
3 G. W. Lee, H. L. Lee, and H. W. Cheong, "Object Detection of AGV in Manufacturing Plants using Deep Learning," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 1, pp. 36-43, Jan. 2021.   DOI
4 N. H. Thang and J. Kim, "Performance Evaluation of YOLO according to Based Feature Extraction Network in Vespa Detection," Journal of Korea Multimedia Society, vol. 24, no. 2, pp. 305-311, Feb. 2021.   DOI
5 J. G. Choe, J. H. Lee, D. G. Kang, and S. H. Seo, "Comparison and Verification of Deep Learning Models for Automatic Recognition of Pills," Journal of Digital Contents Society, vol. 21, no. 3, pp. 445-452, Mar. 2020.   DOI
6 C. H. Lee and Y. J. Jeong, "Estimation of Moving Direction of Objects for Vehicle Tracking in Underground Parking Lot," Journal of Apiculture, vol. 35, no. 3, pp. 149-159, Sept. 2020.   DOI
7 J. S. Kim, C. H. Lee, and S. C. Kwon, "Real-time Online Study Attitude Action Recognition And Dataset Construction," The Conference of The Institute of Electronics and Information Engineers, Jeju, pp. 1133-1135, 2021.
8 G. Y. Yi, Y. J. Kim, S. T. Kim, H. E. Kim, and K. G. Kim, "AR based Beverage Information Visualization and Sharing System using Deep Learning," Journal of Korea Multimedia Society, vol. 22, no. 3, pp. 349-356, Mar. 2019.   DOI
9 D. H. Lee and J. H. Nang, "A Dataset and Keypoint Detection for Club Head Trajectory Estimation in 2D Golf Swing Video," in Proceedings of The Korean Institute of Information Scientists and Engineers Conference, Jeju, pp. 645-647, 2021.
10 D. W. Park, A. R. Goo, and H. J. Park, "Implementation of Region Specific Image Collection And Dataset Building Application Using Roadview API," in Proceedings of The Korean Institute of Information Scientists and Engineers Conference, online, pp. 1424-1426, 2020.
11 S. Y. Lee, Y. J. Kim, Y. B. Shim, H. S. Son, and K. W. Min, "Development of SW technology for recognition, judgement and path control algorithm verification simulation and dataset generation," in Proceedings of Symposium of the Korean Institute of communications and Information Sciences, YongPyong, pp. 353-354, 2020.
12 K. J. Kim, I. S. Jang, and K. T. Lim, "Construction of Wild-fire Smoke Data-set and Comparative Analysis of Detection Method based on Deep Neural Network," in Proceedings of Symposium of the Korean Institute of communications and Information Sciences, YongPyong, pp. 1172-1173, 2021.
13 B. J. Kim, I. K. Kim, H. S. Lim, and J. H. Gwak, "Dataset Augmentation Technique for Crack Detection of Wood Building," Proceedings of the Korean Society of Computer Information Conference, Jeju, pp. 645-647, 2021.
14 H. K. Lim, "Overview of Image-based Object Recognition AI technology for Autonomous Vehicles," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 8, pp. 1117-1123, Aug. 2021.   DOI