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
- 발행연도
- 2020.10
- 수록면
- 1,236 - 1,249 (14page)
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초록· 키워드
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In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.
목차
ABSTRACT
1. INTRODUCTION
2. LSTM NETWORK WITH TRACKING ASSOCIATION
3. EXPERIMENT RESULTS AND DISCUSSION
4. Conclusion and feature work discussion
REFERENCE
참고문헌 (15)
참고문헌 신청
L. Bertinetto, J. Valmadre, J.F. Henriques, A. Vedaldi, and P.H.S. Torr, “Fully-convolutional Siamese Networks for Object Tracking,” Proceeding of the European Conference on Computer Vision, pp. 850-865, 2016.
A. Milan, S.H. Rezatofighi, A. Dick, I. Reid, and K. Schindler “Online Multi-Target Tracking Using Recurrent Neural Networks,” Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence(AAAI-17) , pp. 4225- 4232, 2017.
S. Sun, N. Akhtar, H.S. Song, A. Mian, and M. Shah, “Deep Affinity Network for Multiple Object Tracking,” J ournal of Latex Class Files, Vol. 13, No. 9, pp. 1-15, 2017.
Kh. Farkhodov, K. Oh-Heum, M. Kwang- Seok, K. Oh-Jun, L. Suk-Hwan, and K. Ki- Ryong “A New CSR-DCF Tracking Algorithm based on Faster RCNN Detection Model and CSRT Tracker for Drone Date, J ournal of Korea Multimedia Society, Vol. 22, Iss. 12, pp. 1415-1429, 2019.
A. Jadhav, P. Mukherjee, V. Kaushik, and B. Lall, “Aerial Multi-object Tracking by Detection Using Deep Association Networks,” Proceeding of 2020 National Conference on Communications (NCC) , pp. 1-6, 2020.
A. Milan, S.H. Rezatofighi, A. Dick, I. Reid, and K. Schindler “Online Multi-Target Tracking Using Recurrent Neural Networks,” Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence(AAAI-17) , pp. 4225- 4232, 2017.
S. Sun, N. Akhtar, H.S. Song, A. Mian, and M. Shah, “Deep Affinity Network for Multiple Object Tracking,” J ournal of Latex Class Files, Vol. 13, No. 9, pp. 1-15, 2017.
Kh. Farkhodov, K. Oh-Heum, M. Kwang- Seok, K. Oh-Jun, L. Suk-Hwan, and K. Ki- Ryong “A New CSR-DCF Tracking Algorithm based on Faster RCNN Detection Model and CSRT Tracker for Drone Date, J ournal of Korea Multimedia Society, Vol. 22, Iss. 12, pp. 1415-1429, 2019.
A. Jadhav, P. Mukherjee, V. Kaushik, and B. Lall, “Aerial Multi-object Tracking by Detection Using Deep Association Networks,” Proceeding of 2020 National Conference on Communications (NCC) , pp. 1-6, 2020.
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