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Syst."],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>\n                      Pedestrian trajectory prediction from egocentric monocular video is hindered by camera motion, intermittent occlusions, and complex social interactions. We present NIM-STGCN, a unified framework whose core contribution is a differentiable view normalization(GVN) that couples an enhanced differentiable PnP layer (ED-PnP) with an\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$\\textrm{SE}(3)$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mtext>SE<\/mml:mtext>\n                              <mml:mo>(<\/mml:mo>\n                              <mml:mn>3<\/mml:mn>\n                              <mml:mo>)<\/mml:mo>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      warp to align past observations into a single virtual static camera frame. Because GVN is trained end-to-end, forecasting losses back-propagate to pose estimation, yielding geometrically cleaner inputs. On the normalized histories, a lightweight Gated Convolutional Imputation Module (GCIM) recovers missing bounding-box measurements while preserving observed entries, and an efficient spatio-temporal GCN encodes agent dynamics and interactions (optionally augmented by a physics-guided kinematics\u2013interaction prior, PKIM). A Gaussian-mixture predictor produces multi-modal futures and is optimized with a sequence-level negative log-likelihood together with a time-weighted position loss. Extensive experiments on the JAAD and PIE benchmarks show that NIM-STGCN reduces Average Displacement Error (ADE) and Final Displacement Error (FDE) by 12\u201318 % compared to state-of-the-art methods. Code is available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/fantot\/NIM-STGCN\" ext-link-type=\"uri\">https:\/\/github.com\/fantot\/NIM-STGCN<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s40747-025-02190-3","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T18:45:41Z","timestamp":1766774741000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NIM-STGCN: Differentiable motion decomposition for egocentric pedestrian trajectory prediction"],"prefix":"10.1007","volume":"12","author":[{"given":"Fangtao","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,26]]},"reference":[{"issue":"7","key":"2190_CR1","doi-asserted-by":"publisher","first-page":"4316","DOI":"10.1109\/TITS.2020.3032227","volume":"22","author":"K Muhammad","year":"2020","unstructured":"Muhammad K, Ullah A, Lloret J, Del Ser J, De Albuquerque VHC (2020) Deep learning for safe autonomous driving: Current challenges and future directions. 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