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We propose a novel deep architecture, named DeepFTSG, for robust moving object detection that incorporates single and multi-stream multi-channel <jats:italic>USE-Net trellis<\/jats:italic> asymmetric encoders extending U-Net with squeeze and excitation (SE) blocks and a single shared decoder network for fusing multiple motion and appearance cues. DeepFTSG is a deep learning based approach that builds upon our previous hand-engineered flux tensor split Gaussian (FTSG) change detection video analysis algorithm which won the CDNet CVPR Change Detection Workshop challenge competition. DeepFTSG generalizes much better than top-performing motion detection deep networks, such as the scene-dependent <jats:italic>ensemble-based<\/jats:italic> FgSegNet_v2, while using an order of magnitude fewer weights. Short-term motion and longer-term change cues are estimated using general-purpose unsupervised methods\u2014flux tensor and multi-modal background subtraction, respectively. DeepFTSG was evaluated using the CDnet-2014 change detection challenge dataset, the largest change detection video sequence benchmark with 12.3 billion labeled pixels, and had an overall F-measure of 97%. We also evaluated the cross-dataset <jats:italic>generalization capability<\/jats:italic> of DeepFTSG trained solely on CDnet-2014 short video segments and then evaluated on unseen SBI-2015, LASIESTA and LaSOT benchmark videos. On the unseen SBI-2015 dataset, DeepFTSG had an F-measure accuracy of 87%, more than 30% higher compared to the top-performing deep network FgSegNet_v2 and outperforms the recently proposed KimHa method by 17%. On the unseen LASIESTA, DeepFTSG had an F-measure of 88% and outperformed the best recent deep learning method BSUV-Net2.0 by 3%. On the unseen LaSOT with axis-aligned bounding box ground-truth, network segmentation masks were converted to bounding boxes for evaluation, DeepFTSG had an F-Measure of 55%, outperforming KimHa method by 14% and FgSegNet_v2 by almost 1.5%. When a customized single DeepFTSG model is trained in a scene-dependent manner for comparison with state-of-the-art approaches, then DeepFTSG performs significantly better, reaching an F-Measure of 97% on SBI-2015 (+\u00a010%) and 99% on LASIESTA (+\u00a011%). The source code, pre-trained weights, and video demo for DeepFTSG are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/CIVA-Lab\/DeepFTSG\">https:\/\/github.com\/CIVA-Lab\/DeepFTSG<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s11263-023-01910-x","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T07:02:10Z","timestamp":1697526130000},"page":"776-804","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["DeepFTSG: Multi-stream Asymmetric USE-Net Trellis Encoders with Shared Decoder Feature Fusion Architecture for Video Motion Segmentation"],"prefix":"10.1007","volume":"132","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4961-8229","authenticated-orcid":false,"given":"Gani","family":"Rahmon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kannappan","family":"Palaniappan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imad Eddine","family":"Toubal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Filiz","family":"Bunyak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raghuveer","family":"Rao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guna","family":"Seetharaman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"issue":"3","key":"1910_CR1","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1109\/TITS.2019.2900426","volume":"21","author":"T Akilan","year":"2020","unstructured":"Akilan, T., Wu, Q. 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