{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:03:02Z","timestamp":1784732582792,"version":"3.55.0"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000183","name":"US Army Research Office","doi-asserted-by":"crossref","award":["W911NF2020157"],"award-info":[{"award-number":["W911NF2020157"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:p>Video Camouflaged Object Detection (VCOD) aims to segment objects visually indistinguishable from their background in unconstrained video sequences. This task is challenging due to the extremely low appearance contrast and motion ambiguity caused by camera shifts, occlusion, and background clutter. Although motion cues provide crucial signals to break camouflage, existing methods typically adopt either implicit motion modeling based on initial semantic representations or explicit modeling based on low-level pixel motion, both of which suffer from information loss or noise sensitivity. To this end, we propose IDM-VCOD, an integrated dual-motion VCOD framework that unifies two complementary motion modeling strategies through a dual-motion integrated design. The implicit path aggregates temporal context via spatio-temporal prediction cubes for temporal neighborhood refinement, while the explicit path explicitly aligns multi-frame backgrounds to highlight foreground motion. A selective activation mechanism adaptively triggers the explicit branch only when implicit predictions are unreliable, enhancing accuracy and efficiency. Extensive experiments on the MoCA-Mask and CAD datasets demonstrate that IDM-VCOD achieves superior detection accuracy and generalization compared to state-of-the-art VCOD methods, while significantly reducing model size and inference cost. Comprehensive ablation studies further validate the effectiveness of the dual-motion design and its robustness in challenging camouflage scenarios.<\/jats:p>","DOI":"10.1145\/3820042","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T14:44:02Z","timestamp":1782312242000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An Integrated Dual-Motion Framework for Camouflaged Object Detection in Videos"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2883-7332","authenticated-orcid":false,"given":"Xinyu","family":"Wang","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1116-939X","authenticated-orcid":false,"given":"Hong-Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3303-9185","authenticated-orcid":false,"given":"Zhiruo","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suya","family":"You","sequence":"additional","affiliation":[{"name":"DEVCOM Army Research Laboratory, Adelphi, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5225-0034","authenticated-orcid":false,"given":"Azad M.","family":"Madni","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9474-5035","authenticated-orcid":false,"given":"C.-C. Jay","family":"Kuo","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2024.104160"},{"key":"e_1_3_1_3_2","first-page":"1","volume-title":"Proceedings of the 38th International Conference on Machine Learning (ICML \u201921)","volume":"2","author":"Bertasius Gedas","year":"2021","unstructured":"Gedas Bertasius, Heng Wang, and Lorenzo Torresani. 2021. Is space-time attention all you need for video understanding? In Proceedings of the 38th International Conference on Machine Learning (ICML \u201921), Vol. 2, 1\u20134."},{"key":"e_1_3_1_4_2","first-page":"433","volume-title":"Proceedings of the 14th European Conference on Computer Vision (ECCV \u201916), Part VIII","author":"Bideau Pia","year":"2016","unstructured":"Pia Bideau and Erik Learned-Miller. 2016. It\u2019s moving! A probabilistic model for causal motion segmentation in moving camera videos. In Proceedings of the 14th European Conference on Computer Vision (ECCV \u201916), Part VIII. Springer, 433\u2013449."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2018.2806619"},{"issue":"1","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/116.20240009","article-title":"GreenCOD: A green camouflaged object detection method","volume":"13","author":"Chen Hong-Shuo","year":"2024","unstructured":"Hong-Shuo Chen, Yao Zhu, Suya You, Azad M. Madni, and C.-C. Jay Kuo. 2024. GreenCOD: A green camouflaged object detection method. APSIPA Transactions on Signal and Information Processing 13, 1 (2024), 1\u201328.","journal-title":"APSIPA Transactions on Signal and Information Processing"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01035"},{"key":"e_1_3_1_8_2","first-page":"13864","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Cheng Xuelian","year":"2022","unstructured":"Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, and Zongyuan Ge. 2022. Implicit motion handling for video camouflaged object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 13864\u201313873."},{"key":"e_1_3_1_9_2","first-page":"6339","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Cheng Xuelian","year":"2019","unstructured":"Xuelian Cheng, Yiran Zhong, Yuchao Dai, Pan Ji, and Hongdong Li. 2019. Noise-aware unsupervised deep lidar-stereo fusion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 6339\u20136348."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00616"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.316"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364910365417"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304415.3304515"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3085766"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44267-023-00019-6"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00875"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547912"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02111"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(81)90024-2"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02020-y"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1609\/aaai.v37i1.25167","article-title":"High-resolution iterative feedback network for camouflaged object detection","volume":"37","author":"Hu Xiaobin","year":"2023","unstructured":"Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Donghao Luo, Ying Tai, and Ling Shao. 2023. High-resolution iterative feedback network for camouflaged object detection. Proceedings of the AAAI Conference on Artificial Intelligence 37 (2023), 881\u2013889.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3349209"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3361170"},{"key":"e_1_3_1_27_2","first-page":"19058","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hui Wenjun","year":"2024","unstructured":"Wenjun Hui, Zhenfeng Zhu, Shuai Zheng, and Yao Zhao. 2024. Endow SAM with keen eyes: Temporal-spatial prompt learning for video camouflaged object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 19058\u201319067."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.179"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_14"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-022-1371-y"},{"key":"e_1_3_1_31_2","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"Lamdouar Hala","year":"2020","unstructured":"Hala Lamdouar, Charig Yang, Weidi Xie, and Andrew Zisserman. 2020. Betrayed by motion: Camouflaged object discovery via motion segmentation. In Proceedings of the Asian Conference on Computer Vision."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.04.006"},{"key":"e_1_3_1_33_2","first-page":"13923","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Li Xiangtai","year":"2023","unstructured":"Xiangtai Li, Haobo Yuan, Wenwei Zhang, Guangliang Cheng, Jiangmiao Pang, and Chen Change Loy. 2023. Tube-link: A flexible cross tube framework for universal video segmentation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 13923\u201313933."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02051-5"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2893843"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.39"},{"key":"e_1_3_1_38_2","first-page":"1857","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Nawfal Meeran Muhammad","year":"2024","unstructured":"Muhammad Nawfal Meeran, Gokul Adethya T, and Bhanu Pratyush Mantha, 2024. SAM-PM: Enhancing video camouflaged object detection using spatio-temporal attention. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 1857\u20131866."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3381445"},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"6660","DOI":"10.1109\/TIP.2024.3498326","article-title":"Salient object detection in RGB-D videos","author":"Mou Ao","year":"2024","unstructured":"Ao Mou, Yukang Lu, Jiahao He, Dingyao Min, Keren Fu, and Qijun Zhao. 2024. Salient object detection in RGB-D videos. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society 33 (2024), 6660\u20136675.","journal-title":"IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00220"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3417329"},{"key":"e_1_3_1_43_2","first-page":"3099","volume-title":"Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583)","volume":"4","author":"Piccardi Massimo","year":"2004","unstructured":"Massimo Piccardi. 2004. Background subtraction techniques: A review. In Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), Vol. 4. IEEE, 3099\u20133104."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00766"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01146"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3264883"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00931"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110302"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3051099"},{"key":"e_1_3_1_51_2","first-page":"162","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"Xie Junyu","year":"2024","unstructured":"Junyu Xie, Charig Yang, Weidi Xie, and Andrew Zisserman. 2024. Moving object segmentation: All you need is SAM (and flow). In Proceedings of the Asian Conference on Computer Vision, 162\u2013178."},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03342-1"},{"key":"e_1_3_1_53_2","first-page":"7284","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Yan Pengxiang","year":"2019","unstructured":"Pengxiang Yan, Guanbin Li, Yuan Xie, Zhen Li, Chuan Wang, Tianshui Chen, and Liang Lin. 2019. Semi-supervised video salient object detection using pseudo-labels. In Proceedings of the IEEE International Conference on Computer Vision, 7284\u20137293."},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00709"},{"key":"e_1_3_1_55_2","first-page":"2875","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP \u201924)","author":"Yu Zifan","year":"2024","unstructured":"Zifan Yu, Erfan Bank Tavakoli, Meida Chen, Suya You, Raghuveer Rao, Sanjeev Agarwal, and Fengbo Ren. 2024. TokenMotion: Motion-guided vision transformer for video camouflaged object detection via learnable token selection. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP \u201924). IEEE, 2875\u20132879."},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","unstructured":"Xin Zhang Tao Xiao Ge-Peng Ji Xuan Wu Keren Fu and Qijun Zhao. 2025. Explicit motion handling and interactive prompting for video camouflaged object detection. IEEE Transactions on Image Processing 34 (2025) 2853\u20132866.","DOI":"10.1109\/TIP.2025.3565879"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3348659"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3223955"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.52"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3820042","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T14:27:07Z","timestamp":1784730427000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3820042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":58,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,31]]}},"alternative-id":["10.1145\/3820042"],"URL":"https:\/\/doi.org\/10.1145\/3820042","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,22]]},"assertion":[{"value":"2025-05-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}