arXiv:2609.18836v1 Announce Type: new Abstract: A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomal...
arXiv:2606. 14724v1 Announce Type: cross Abstract: Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both.
arXiv:2611.06455v1 Announce Type: new Abstract: Analytics on video archives are bottlenecked by decoding. In a dense pipeline that runs a detector on every frame, decoding is a small fraction of the...
MoG-VLM is a motion‑guided vision‑language model designed for efficient video anomaly detection. It uses a lightweight motion signal, Temporal Gradient Magnitude, to perform content‑aware sampling and activates dense multi‑frame VLM reasoning only around frames with high anomaly likelihood via a hierarchical dual‑stream design. The method achieves state‑of‑the‑art AUC‑ROC scores on four benchmarks and delivers a 1.31× inference speedup on XD‑Violence.
Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotations but require substantial in-domain data.
PERSIST redefines shot boundary detection as a task of semantic discrimination, requiring a persistent update of a video’s latent temporal state rather than a transient visual change. It employs a FiLM‑conditioned sinusoidal representation network and a structured discriminator that fuses local change, transient impulse, and return‑to‑trend cues into a single interpretable per‑frame signal. The method achieves comparable recall to leading detectors while significantly reducing false positives from flash, text overlay, and archival artifacts, and it is trained solely on real transitions from ClipShots.
arXiv:2605.12006v2 Announce Type: replace Abstract: The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deploymen...
arXiv:2412.03044v3 Announce Type: replace Abstract: Video anomaly detection (VAD) is a vital yet complex open-set task in computer vision, commonly tackled through reconstruction-based methods. Howev...
We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data built primarily from Ego4D. Given a monocular video and an event-triggered prompt of the form "when X happens, do Y," EgoPlay infers whether and when event X occurs, preserves pre-event frames, and applies edit Y only to the post-event continuation.
Efficient Zero‑Shot Video Anomaly Detection via Signal‑Aware Adaptive Fusion (SAAF) introduces a framework that leverages dual‑signal adaptive sampling—delta and drift signals—to detect sudden and gradual anomalies without training data. It employs a threshold‑gated cascaded architecture that first applies lightweight frame‑level scoring before invoking costly video‑level analysis only when needed, and a signal‑aware weighted fusion mechanism that adapts score combination based on the triggering signal. Across four benchmarks, SAAF achieves state‑of‑the‑art accuracy on XD‑Violence, MSAD, and ShanghaiTech while delivering significant throughput improvements over prior zero‑shot methods.
arXiv:2607. 24560v1 Announce Type: cross Abstract: We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data built primarily from Ego4D.
The paper evaluates event-triggered recording strategies for static surveillance cameras, comparing six motion-based triggers, a CNN detector, strided detection, and hybrid approaches on a benchmark of seven scenes. Detector-based triggers reduce idle frame storage to 19‑28% versus 36‑89% for motion triggers, and remain robust under camera jitter, keeping under 29% idle frames. In addition, the study shows that increasing YOLO11n input resolution improves detection accuracy more than larger models, and that longer track memory boosts tracking IDF1, though detection remains the limiting factor.