메뉴 건너뛰기




Volumn 2017-January, Issue , 2017, Pages 889-897

Object detection in videos with tubelet proposal networks

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LONG SHORT-TERM MEMORY; OBJECT RECOGNITION; TUBES (COMPONENTS);

EID: 85041925966     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.101     Document Type: Conference Paper
Times cited : (206)

References (34)
  • 1
    • 84911445394 scopus 로고    scopus 로고
    • Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning
    • 1
    • S.-H. Bae and K.-J. Yoon. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. CVPR, 2014. 1
    • (2014) CVPR
    • Bae, S.-H.1    Yoon, K.-J.2
  • 2
    • 84956678436 scopus 로고    scopus 로고
    • DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
    • 1
    • L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. In ICLR, 2015. 1
    • (2015) ICLR
    • Chen, L.-C.1    Papandreou, G.2    Kokkinos, I.3    Murphy, K.4    Yuille, A.L.5
  • 3
    • 79959728283 scopus 로고    scopus 로고
    • Localizing objects while learning their appearance
    • 2, 6
    • T. Deselaers, B. Alexe, and V. Ferrari. Localizing Objects While Learning Their Appearance. ECCV, 2010. 2, 6
    • (2010) ECCV
    • Deselaers, T.1    Alexe, B.2    Ferrari, V.3
  • 6
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • 1, 2
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR, 2014. 1, 2
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 9
    • 85030262627 scopus 로고    scopus 로고
    • Learning to track at 100 FPS with deep regression networks
    • 5
    • D. Held, S. Thrun, and S. Savarese. Learning to Track at 100 FPS with Deep Regression Networks. In ECCV, 2016. 5
    • (2016) ECCV
    • Held, D.1    Thrun, S.2    Savarese, S.3
  • 10
    • 84922907906 scopus 로고    scopus 로고
    • High-speed tracking with kernelized correlation filters
    • 7, 8
    • J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. High-speed tracking with kernelized correlation filters. TPAMI, 2015. 7, 8
    • (2015) TPAMI
    • Henriques, J.F.1    Caseiro, R.2    Martins, P.3    Batista, J.4
  • 13
    • 84943738421 scopus 로고    scopus 로고
    • Efficient image and video Co-localization with frank-wolfe algorithm
    • 2, 8
    • A. Joulin, K. Tang, and L. Fei-Fei. Efficient Image and Video Co-localization with Frank-Wolfe Algorithm. ECCV, 2014. 2, 8
    • (2014) ECCV
    • Joulin, A.1    Tang, K.2    Fei-Fei, L.3
  • 15
    • 84986331475 scopus 로고    scopus 로고
    • Object detection from video tubelets with convolutional neural networks
    • 1, 2, 5, 8
    • K. Kang, W. Ouyang, H. Li, and X. Wang. Object detection from video tubelets with convolutional neural networks. In CVPR, 2016. 1, 2, 5, 8
    • (2016) CVPR
    • Kang, K.1    Ouyang, W.2    Li, H.3    Wang, X.4
  • 17
    • 84973884868 scopus 로고    scopus 로고
    • Unsupervised object discovery and tracking in video collections
    • 8
    • S. Kwak, M. Cho, I. Laptev, J. Ponce, and C. Schmid. Unsupervised Object Discovery and Tracking in Video Collections. ICCV, 2015. 8
    • (2015) ICCV
    • Kwak, S.1    Cho, M.2    Laptev, I.3    Ponce, J.4    Schmid, C.5
  • 18
    • 85041893972 scopus 로고    scopus 로고
    • Person search with natural language description
    • 1
    • S. Li, T. Xiao, H. Li, B. Zhou, D. Yue, and X. Wang. Person search with natural language description. In CVPR, 2017. 1
    • (2017) CVPR
    • Li, S.1    Xiao, T.2    Li, H.3    Zhou, B.4    Yue, D.5    Wang, X.6
  • 19
    • 85044466079 scopus 로고    scopus 로고
    • Vip-cnn: A visual phrase reasoning convolutional neural network for visual relationship detection
    • 1
    • Y. Li, W. Ouyang, and X. Wang. Vip-cnn: A visual phrase reasoning convolutional neural network for visual relationship detection. In CVPR, 2017. 1
    • (2017) CVPR
    • Li, Y.1    Ouyang, W.2    Wang, X.3
  • 20
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • 1
    • J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 1
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 22
    • 84866674032 scopus 로고    scopus 로고
    • Learning object class detectors from weakly annotated video
    • 2, 6, 8
    • A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari. Learning object class detectors from weakly annotated video. CVPR, 2012. 2, 6, 8
    • (2012) CVPR
    • Prest, A.1    Leistner, C.2    Civera, J.3    Schmid, C.4    Ferrari, V.5
  • 24
    • 84960980241 scopus 로고    scopus 로고
    • Faster r-cnn: Towards real-time object detection with region proposal networks
    • 1,3
    • S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. NIPS, 2015. 1, 3
    • (2015) NIPS
    • Ren, S.1    He, K.2    Girshick, R.3    Sun, J.4
  • 25
    • 84959200559 scopus 로고    scopus 로고
    • Deeply learned attributes for crowded scene understanding
    • 1
    • J. Shao, K. Kang, C. Change Loy, and X. Wang. Deeply learned attributes for crowded scene understanding. In CVPR, 2015. 1
    • (2015) CVPR
    • Shao, J.1    Kang, K.2    Change Loy, C.3    Wang, X.4
  • 26
    • 84986254030 scopus 로고    scopus 로고
    • Slicing convolutional neural network for crowd video understanding
    • 1
    • J. Shao, C.-C. Loy, K. Kang, and X. Wang. Slicing convolutional neural network for crowd video understanding. In CVPR, 2016. 1
    • (2016) CVPR
    • Shao, J.1    Loy, C.-C.2    Kang, K.3    Wang, X.4
  • 27
    • 85083953063 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • 1
    • K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. ICLR, 2015. 1
    • (2015) ICLR
    • Simonyan, K.1    Zisserman, A.2
  • 28
    • 84928547704 scopus 로고    scopus 로고
    • Sequence to sequence learning with neural networks
    • 6
    • I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NIPS, 2014. 6
    • (2014) NIPS
    • Sutskever, I.1    Vinyals, O.2    Le, Q.V.3
  • 31
    • 84973856013 scopus 로고    scopus 로고
    • Visual tracking with fully convolutional networks
    • 1
    • L. Wang, W. Ouyang, X. Wang, and H. Lu. Visual tracking with fully convolutional networks. ICCV, 2015. 1
    • (2015) ICCV
    • Wang, L.1    Ouyang, W.2    Wang, X.3    Lu, H.4
  • 32
    • 85041899319 scopus 로고    scopus 로고
    • Joint detection and identification feature learning for person search
    • 1
    • T. Xiao, S. Li, B. Wang, L. Lin, and X. Wang. Joint detection and identification feature learning for person search. In CVPR, 2017. 1
    • (2017) CVPR
    • Xiao, T.1    Li, S.2    Wang, B.3    Lin, L.4    Wang, X.5
  • 33
    • 85026925275 scopus 로고    scopus 로고
    • Crowd tracking with dynamic evolution of group structures
    • 1
    • F. Zhu, X. Wang, and N. Yu. Crowd tracking with dynamic evolution of group structures. In ECCV, 2014. 1
    • (2014) ECCV
    • Zhu, F.1    Wang, X.2    Yu, N.3
  • 34
    • 84952018709 scopus 로고    scopus 로고
    • Edge boxes: Locating object proposals from edges
    • 3
    • C. L. Zitnick and P. Dollar. Edge Boxes: Locating Object Proposals from Edges. ECCV, 2014. 3
    • (2014) ECCV
    • Zitnick, C.L.1    Dollar, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.