Detecting temporally consistent objects in videos through object class label propagation

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Standard

Detecting temporally consistent objects in videos through object class label propagation. / Tripathi, Subarna; Belongie, Serge; Hwang, Youngbae; Nguyen, Truong.

I: 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, 23.05.2016.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Tripathi, S, Belongie, S, Hwang, Y & Nguyen, T 2016, 'Detecting temporally consistent objects in videos through object class label propagation', 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. https://doi.org/10.1109/WACV.2016.7477702

APA

Tripathi, S., Belongie, S., Hwang, Y., & Nguyen, T. (2016). Detecting temporally consistent objects in videos through object class label propagation. 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. https://doi.org/10.1109/WACV.2016.7477702

Vancouver

Tripathi S, Belongie S, Hwang Y, Nguyen T. Detecting temporally consistent objects in videos through object class label propagation. 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. 2016 maj 23. https://doi.org/10.1109/WACV.2016.7477702

Author

Tripathi, Subarna ; Belongie, Serge ; Hwang, Youngbae ; Nguyen, Truong. / Detecting temporally consistent objects in videos through object class label propagation. I: 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. 2016.

Bibtex

@inproceedings{a4a6f5360b674ce3b272c2bedca10a12,

title = "Detecting temporally consistent objects in videos through object class label propagation",

abstract = "Object proposals for detecting moving or static video objects need to address issues such as speed, memory complexity and temporal consistency. We propose an efficient Video Object Proposal (VOP) generation method and show its efficacy in learning a better video object detector A deep-learning based video object detector learned using the proposed VOP achieves state-of-the-art detection performance on the Youtube-Objects dataset. We further propose a clustering of VOPs which can efficiently be used for detecting objects in video in a streaming fashion. As opposed to applying per-frame convolutional neural network (CNN) based object detection, our proposed method called Objects in Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a small fraction of all candidate proposals in every video frame through streaming clustering of object proposals and class-label propagation. Source code for VOP clustering is available at https://github. com/subtri/streaming-VOP-clustering.",

author = "Subarna Tripathi and Serge Belongie and Youngbae Hwang and Truong Nguyen",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; IEEE Winter Conference on Applications of Computer Vision, WACV 2016 ; Conference date: 07-03-2016 Through 10-03-2016",

year = "2016",

month = may,

day = "23",

doi = "10.1109/WACV.2016.7477702",

language = "English",

journal = "2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016",

}

RIS

TY - GEN

T1 - Detecting temporally consistent objects in videos through object class label propagation

AU - Tripathi, Subarna

AU - Belongie, Serge

AU - Hwang, Youngbae

AU - Nguyen, Truong

PY - 2016/5/23

Y1 - 2016/5/23

N2 - Object proposals for detecting moving or static video objects need to address issues such as speed, memory complexity and temporal consistency. We propose an efficient Video Object Proposal (VOP) generation method and show its efficacy in learning a better video object detector A deep-learning based video object detector learned using the proposed VOP achieves state-of-the-art detection performance on the Youtube-Objects dataset. We further propose a clustering of VOPs which can efficiently be used for detecting objects in video in a streaming fashion. As opposed to applying per-frame convolutional neural network (CNN) based object detection, our proposed method called Objects in Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a small fraction of all candidate proposals in every video frame through streaming clustering of object proposals and class-label propagation. Source code for VOP clustering is available at https://github. com/subtri/streaming-VOP-clustering.

AB - Object proposals for detecting moving or static video objects need to address issues such as speed, memory complexity and temporal consistency. We propose an efficient Video Object Proposal (VOP) generation method and show its efficacy in learning a better video object detector A deep-learning based video object detector learned using the proposed VOP achieves state-of-the-art detection performance on the Youtube-Objects dataset. We further propose a clustering of VOPs which can efficiently be used for detecting objects in video in a streaming fashion. As opposed to applying per-frame convolutional neural network (CNN) based object detection, our proposed method called Objects in Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a small fraction of all candidate proposals in every video frame through streaming clustering of object proposals and class-label propagation. Source code for VOP clustering is available at https://github. com/subtri/streaming-VOP-clustering.

UR - http://www.scopus.com/inward/record.url?scp=84977665623&partnerID=8YFLogxK

U2 - 10.1109/WACV.2016.7477702

DO - 10.1109/WACV.2016.7477702

M3 - Conference article

AN - SCOPUS:84977665623

JO - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

JF - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Y2 - 7 March 2016 through 10 March 2016

ER -

ID: 301828559

Datalogisk Institut