{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:59:22Z","timestamp":1760241562804,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T00:00:00Z","timestamp":1526947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61629301, 61773312, and 61503296"],"award-info":[{"award-number":["61629301, 61773312, and 61503296"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017T100752 and 2015M572563"],"award-info":[{"award-number":["2017T100752 and 2015M572563"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Inspired by the recent spatio-temporal action localization efforts with tubelets (sequences of bounding boxes), we present a new spatio-temporal action localization detector Segment-tube, which consists of sequences of per-frame segmentation masks. The proposed Segment-tube detector can temporally pinpoint the starting\/ending frame of each action category in the presence of preceding\/subsequent interference actions in untrimmed videos. Simultaneously, the Segment-tube detector produces per-frame segmentation masks instead of bounding boxes, offering superior spatial accuracy to tubelets. This is achieved by alternating iterative optimization between temporal action localization and spatial action segmentation. Experimental results on three datasets validated the efficacy of the proposed method, including (1) temporal action localization on the THUMOS 2014 dataset; (2) spatial action segmentation on the Segtrack dataset; and (3) joint spatio-temporal action localization on the newly proposed ActSeg dataset. It is shown that our method compares favorably with existing state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s18051657","type":"journal-article","created":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T03:14:24Z","timestamp":1527045264000},"page":"1657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6636-6396","authenticated-orcid":false,"given":"Le","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, Xi\u2019an, Shannxi 710049, China"}]},{"given":"Xuhuan","family":"Duan","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, Xi\u2019an, Shannxi 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7917-9749","authenticated-orcid":false,"given":"Qilin","family":"Zhang","sequence":"additional","affiliation":[{"name":"HERE Technologies, Chicago, IL 60606, USA"}]},{"given":"Zhenxing","family":"Niu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou 311121, China"}]},{"given":"Gang","family":"Hua","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA 98052, USA"}]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, Xi\u2019an, Shannxi 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,22]]},"reference":[{"key":"ref_1","first-page":"2","article-title":"Action recognition and detection by combining motion and appearance features","volume":"1","author":"Wang","year":"2014","journal-title":"THUMOS14 Action Recognit. 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