GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids

[2603.25467] GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25467 (cs) [Submitted on 26 Mar 2026 ( v1 ), last revised 14 Aug 2026 (this version, v4)] Title: GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids Authors: Mohamed Eltahir , Ahmed O. Ibrahim , Obada Siralkhatim , Tabarak Abdallah , Sondos Mohamed View a PDF of the paper titled GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids, by Mohamed Eltahir and 4 other authors View PDF HTML (experimental) Abstract: Vision-Language Models (VLMs) are powerful open-set reasoners, yet their direct use as anomaly detectors in video surveillance is fragile: without calibrated anomaly priors, they alternate between missed detections and hallucinated false alarms. We argue the problem is not the VLM itself but how it is used. VLMs should function as anomaly proposers, generating open-set candidate descriptions that are then grounded and tracked by purpose-built spatial and temporal modules. We instantiate this propose-ground-propagate principle in GridVAD, a training-free pipeline that produces pixel-level anomaly masks without any domain-specific training. A VLM reasons over stratified grid representations of video clips to generate natural-language anomaly proposals. Self-Consistency Consolidation (SCC) filters hallucinations by retaining only proposals that recur across multiple independent samplings. Grounding DINO anchors each surviving proposal to a bounding box, and SAM2 propagates it as a dense mask through the anomaly interval. The per-clip model budget is fixed at M+1 calls regardless of video length, where M can be set according to the proposals needed. On UCSD Ped2, GridVAD achieves the highest Pixel-AUROC (77.59) among all compared methods, surpassing even the partially fine-tuned TAO (75.11) and outperforms other zero-shot approaches on object-level RBDC by over 5x. Ablations reveal that SCC provides a controllable precision-recall tradeoff: filtering improves all pixel level metrics at a modest cost in object-level recall. Efficiency experiments show GridVAD is 2.7x more call-efficient than uniform per-frame VLM querying while additionally producing dense segmentation this http URL and qualitative video results are available at this https URL . Comments: Accepted at the Large-scale Video Object Segmentation (LVOS) Workshop in conjunction with ECCV 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.25467 [cs.CV] (or arXiv:2603.25467v4 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.25467 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sondos Mohamed [ view email ] [v1] Thu, 26 Mar 2026 14:08:41 UTC (1,591 KB) [v2] Thu, 2 Apr 2026 07:53:39 UTC (1,591 KB) [v3] Tue, 11 Aug 2026 08:00:15 UTC (1,570 KB) [v4] Fri, 14 Aug 2026 20:19:12 UTC (1,573 KB) Full-text links: Access Paper: View a PDF of the paper titled GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids, by Mohamed Eltahir and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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