arXiv:2506. 03162v3 Announce Type: replace-cross Abstract: The rapid proliferation of surveillance cameras has increased the demand for automated violence detection.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2607. 03131v1 Announce Type: cross Abstract: Modern video surveillance systems generate far more video streams than human operators can effectively monitor, making automated analysis essential for timely detection of security events.
arXiv:2608.29759v1 Announce Type: cross Abstract: We present SynCrash, a multi-stage pipeline for zero-shot accident detection, spatial localization, and collision-type classification in fixed-view C...
arXiv:2606. 17437v1 Announce Type: cross Abstract: Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges.
arXiv:2604. 27975v2 Announce Type: replace-cross Abstract: Traditional Shot Boundary Detection (SBD) inherently struggles with complex transitions by formulating the task around isolated cut points, frequently yielding corrupted video shots.
The paper introduces a Short-Window Sliding Learning framework for real‑time violence detection in CCTV footage. It splits videos into 1–2 second clips and uses LLM‑based auto‑caption labeling to build fine‑grained datasets, preserving temporal continuity for accurate recognition of rapid violent events. The method achieves 95.25% accuracy on RWF‑2000 and 83.25% on UCF‑Crime, demonstrating strong generalization and real‑time applicability in intelligent surveillance systems.
arXiv:2410. 19553v2 Announce Type: replace-cross Abstract: This paper explores the impact of occlusions in video action detection.