The paper introduces a lightweight two‑stage system for real‑time video anomaly detection. First, YOLO v11n‑pose detects people and extracts seventeen skeletal keypoints in a single forward pass. Second, each cropped person region is encoded with CLIP ViT‑B/32 and compared via cosine similarity to predefined textual descriptions of anomalous behaviors, removing the need for optical flow, separate pose estimators, or density‑based scoring. Experiments on CUHK Avenue, ShanghaiTech Campus, and a custom indoor dataset achieve about 51 FPS on an NVIDIA Titan XP, a 3.36× speedup over a multi‑feature baseline, while preserving high frame‑level AUROC scores (89.26%, 70.26%, and 84.13%).
arXiv:2605.21957v2 Announce Type: replace Abstract: Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variatio...
arXiv:2608. 19987v1 Announce Type: new Abstract: Skeleton-based Video Anomaly Detection (VAD) offers a robust, privacy-preserving solution for identifying abnormal behaviors.
Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, viewpoint, and human appearance. To mitigate visual noise and address privacy concerns, recent work has shifted to pose-based VAD, which focuses on motion dynamics rather than raw video data.
arXiv:2608. 05069v1 Announce Type: cross Abstract: Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, viewpoint, and human appearance.
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:2607. 18142v1 Announce Type: cross Abstract: Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems.
RoboVAD is a large-scale benchmark for video anomaly detection in robotic arm manipulation, featuring cross-domain evaluation where both tasks and anomaly types are unseen during training. The dataset challenges existing VAD methods, with state-of-the-art approaches, including a new method tailored for robotic arms, still achieving less than 70% micro-averaged frame-level AUC in the hardest setting. The authors provide the dataset and code publicly for further research.
arXiv:2607. 09114v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.
The paper investigates how weakly supervised video anomaly detectors, trained with only video‑level labels, are evaluated using frame‑level metrics such as Micro‑AUROC and AP. It shows that these metrics largely measure a detector’s ability to separate different videos rather than correctly ordering anomalous moments within a single video, a phenomenon termed temporal dilution. Experiments demonstrate that a detector can achieve high pooled scores even when it assigns the same score to every frame in a video, indicating that current evaluation practices may overstate temporal localization performance.
Event cameras, also known as neuromorphic cameras, have gained significant attention in recent years due to their high temporal resolution, high dynamic range, and low power consumption. While many studies and datasets in neuromorphic vision have focused on automotive and drone applications, human-centric daily-life scenarios remain largely underrepresented, despite their importance for developing and benchmarking event-based perception systems.
arXiv:2505.02393v3 Announce Type: replace Abstract: Most existing video anomaly detectors rely on RGB frames alone, which limit their ability to capture abrupt or transient motion cues that are criti...
The paper introduces TITAnD, a Trajectory Image Transformer that converts dense and sparse GPS trajectories into a Hyperspectral Trajectory Image (HTI) and applies vision-based classification and segmentation for anomaly detection. It employs a Cyclic Factorized Transformer (CFT) that splits attention along within-day and across-day axes, drastically reducing computational cost and enabling multi-month analysis. Empirical results show TITAnD outperforms existing sparse and dense benchmarks, achieving higher AUC-PR and faster inference than comparable Transformers.