Temporally enhanced image object proposals for online video object and action detections
Despite the recent advances of image object proposals (IOPs) and video object proposals (VOPs), it still remains a challenge to apply them to online video object/action detection. To address this problem, we propose a novel form of image object proposals, Temporally Enhanced Image Object Proposals (...
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sg-ntu-dr.10356-1421192020-06-16T03:20:48Z Temporally enhanced image object proposals for online video object and action detections Yang, Jiong Yuan, Junsong Interdisciplinary Graduate School (IGS) Engineering::Computer science and engineering Video Proposal Despite the recent advances of image object proposals (IOPs) and video object proposals (VOPs), it still remains a challenge to apply them to online video object/action detection. To address this problem, we propose a novel form of image object proposals, Temporally Enhanced Image Object Proposals (TE-IOPs), for online video object/action detection. The proposed TE-IOPs augment the existing IOPs at every frame by their temporal dynamics in the past few frames. We develop a dynamic programming scheme to efficiently search for such TE-IOPs in an online manner. Compared with existing VOPs that cannot run online, our TE-IOPs can be used for online detection. Compared with IOPs, our TE-IOPs bring rich temporal dynamics with minor computational cost. Experiments on benchmark datasets validate the superior performance of the proposed TE-IOPs over existing IOPs and VOPs, in terms of both the proposal re-ranking and the application of online action detection. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) 2020-06-16T03:20:48Z 2020-06-16T03:20:48Z 2018 Journal Article Yang, J., & Yuan, J. (2018). Temporally enhanced image object proposals for online video object and action detections. Journal of Visual Communication and Image Representation, 53, 245-256. doi:10.1016/j.jvcir.2018.03.018 1047-3203 https://hdl.handle.net/10356/142119 10.1016/j.jvcir.2018.03.018 2-s2.0-85045451673 53 245 256 en Journal of Visual Communication and Image Representation © 2018 Elsevier Inc. All rights reserved. |
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Engineering::Computer science and engineering Video Proposal Yang, Jiong Yuan, Junsong Temporally enhanced image object proposals for online video object and action detections |
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Despite the recent advances of image object proposals (IOPs) and video object proposals (VOPs), it still remains a challenge to apply them to online video object/action detection. To address this problem, we propose a novel form of image object proposals, Temporally Enhanced Image Object Proposals (TE-IOPs), for online video object/action detection. The proposed TE-IOPs augment the existing IOPs at every frame by their temporal dynamics in the past few frames. We develop a dynamic programming scheme to efficiently search for such TE-IOPs in an online manner. Compared with existing VOPs that cannot run online, our TE-IOPs can be used for online detection. Compared with IOPs, our TE-IOPs bring rich temporal dynamics with minor computational cost. Experiments on benchmark datasets validate the superior performance of the proposed TE-IOPs over existing IOPs and VOPs, in terms of both the proposal re-ranking and the application of online action detection. |
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Interdisciplinary Graduate School (IGS) |
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Interdisciplinary Graduate School (IGS) Yang, Jiong Yuan, Junsong |
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Article |
author |
Yang, Jiong Yuan, Junsong |
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Yang, Jiong |
title |
Temporally enhanced image object proposals for online video object and action detections |
title_short |
Temporally enhanced image object proposals for online video object and action detections |
title_full |
Temporally enhanced image object proposals for online video object and action detections |
title_fullStr |
Temporally enhanced image object proposals for online video object and action detections |
title_full_unstemmed |
Temporally enhanced image object proposals for online video object and action detections |
title_sort |
temporally enhanced image object proposals for online video object and action detections |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/142119 |
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1681058931737100288 |