지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.9 No.6
- 발행연도
- 2020.12
- 수록면
- 427 - 434 (8page)
- DOI
- 10.5573/IEIESPC.2020.9.6.427
이용수
초록· 키워드
오류제보하기
Motion capturing involves the identification of objects in motion and plotting their motion by analyzing them. When considered in regard to a video sequence, motion capturing can be defined as a procedure of unmasking objects in motion in sequences of frames using exhaustive and proficient digital image processing techniques. Methodology: To ingress need contrasting time for trial and their execution shows difference in terms of pace and memory requisites. Results: The result obtained shows the performance of the algorithms and models in the given conditions. It also reflects the best suited environment for the techniques. The work was accomplished by testing the algorithms for numerous sequences of input video. The identification rate analysis gives the identification rate of foreground pixels for various colors divergent from the background model. Conclusions: The objective of computer vision is to imitate human vision utilizing advanced digital images through three principle handling parts that are executed consistently, i.e., acquisition of pictures, picture processing, and picture investigation and comprehension. It has gained a lot of attention from researchers in an enormous field. The basic purpose of this study was to prospecting tracking and computer-aided object detection techniques. For this purpose, a number of methods were observed, studied critically, and used. Originality: In the study, an improved frame differencing method for tracking an object is briefly reviewed, and an improved version of the algorithm was also implemented using MATLAB. The proposed algorithm was tested over distinct video sequences, and it was observed that the objects in motion were identified with minimal error rate when compared to a traditional frame differencing method. Limitations: One of the major challenges in the process of tracking objects in motion is to design algorithms or techniques for tracking the objects present in disrupted or random videos, like videos attained from broadcast news networks or home videos. These videos contain noise, and some of them may be unstructured, compressed, and denominationally having edited pieces obtained in different ways by moving cameras.
목차
Abstract
1. Introduction
2. Literature Review
3. Methodology
4. Results
5. Discussion
6. Conclusion
7. Future Scopes
References
참고문헌 (16)
참고문헌 신청
J.S. Zhang / 2017 / Application of deep learning and unmanned aerial vehicle technology in traffic flow monitoring / IEEE International Conference on Machine Learning & Cybernetics (ICMLC 2017) :189~194
Lili Huang / 2010 / Real-Time Multi-Vehicle Detection and Sub-Feature Based Tracking for Traffic Surveillance Systems / 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
F. Farahi / 2020 / Probabilistic Kalman filter for moving object tracking / Signal Process-Image 82:115751~1-16
S. Ren / 2017 / Faster R-CNN:towards real-time object detection with region proposal networks / IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2017) 39(6):1137~1149
S. Park / 2019 / Patch-Based crack detection in black box images using convolutional neural networks / J. Comput. Civil Eng 33(3):04019017~1-11
Lili Huang / 2010 / Real-Time Multi-Vehicle Detection and Sub-Feature Based Tracking for Traffic Surveillance Systems / 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
F. Farahi / 2020 / Probabilistic Kalman filter for moving object tracking / Signal Process-Image 82:115751~1-16
S. Ren / 2017 / Faster R-CNN:towards real-time object detection with region proposal networks / IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2017) 39(6):1137~1149
S. Park / 2019 / Patch-Based crack detection in black box images using convolutional neural networks / J. Comput. Civil Eng 33(3):04019017~1-11
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