Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline

• URL: http://arxiv.org/abs/2506.05175v1
• Date: Thu, 05 Jun 2025 15:49:39 GMT
• Title: Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline
• Authors: Yuzhi Huang, Chenxin Li, Haitao Zhang, Zixu Lin, Yunlong Lin, Hengyu Liu, Wuyang Li, Xinyu Liu, Jiechao Gao, Yue Huang, Xinghao Ding, Yixuan Yuan,
• Abstract summary: A new framework called Track Any Anomalous Object (TAO) introduces a granular video anomaly detection pipeline.<n>Unlike methods that assign anomaly scores to every pixel, our approach transforms the problem into pixel-level tracking of anomalous objects.<n>Experiments demonstrate that TAO sets new benchmarks in accuracy and robustness.
• Score: 63.96226274616927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Although existing methods have primarily focused on detecting anomalous objects in videos -- either by identifying anomalous frames or objects -- they often neglect finer-grained analysis, such as anomalous pixels, which limits their ability to capture a broader range of anomalies. To address this challenge, we propose a new framework called Track Any Anomalous Object (TAO), which introduces a granular video anomaly detection pipeline that, for the first time, integrates the detection of multiple fine-grained anomalous objects into a unified framework. Unlike methods that assign anomaly scores to every pixel, our approach transforms the problem into pixel-level tracking of anomalous objects. By linking anomaly scores to downstream tasks such as segmentation and tracking, our method removes the need for threshold tuning and achieves more precise anomaly localization in long and complex video sequences. Experiments demonstrate that TAO sets new benchmarks in accuracy and robustness. Project page available online.

Related papers

• ComplexVAD: Detecting Interaction Anomalies in Video [45.08126325125808]
  We introduce a new large-scale anomaly detection dataset: ComplexVAD.<n>In addition, we propose a method to detect complex anomalies via modeling interactions between objects using a scene graph with video attributes.<n>With our proposed method and two other state-of-the-art video anomaly detection methods, we obtain baseline scores on ComplexVAD and demonstrate that our new method outperforms existing works.
  arXiv  Detail & Related papers  (2025-01-16T18:35:45Z)
• Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts [57.01985221057047]
  This paper introduces a novel method that learnstemporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) based on pre-trained vision-language models (VLMs) Our method achieves state-of-theart performance on three public benchmarks for the WSVADL task.
  arXiv  Detail & Related papers  (2024-08-12T03:31:29Z)
• View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis [0.0]
  We introduce and formalize Scene Anomaly Detection (Scene AD) as the task of unsupervised, pixel-wise anomaly localization.<n>We evaluate progress in Scene AD using ToyCity, the first multi-object, multi-view real-image dataset.<n>Our experiments demonstrate that OmniAD, when used with augmented views, yields a 64.33% increase in pixel-wise (F_1) score over Reverse Distillation with no augmentation.
  arXiv  Detail & Related papers  (2024-06-26T01:54:10Z)
• Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection [103.92970668001277]
  We propose a Dynamic Erasing Network (DE-Net) for weakly supervised video anomaly detection. We first propose a multi-scale temporal modeling module, capable of extracting features from segments of varying lengths. Then, we design a dynamic erasing strategy, which dynamically assesses the completeness of the detected anomalies.
  arXiv  Detail & Related papers  (2023-12-04T09:40:11Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection [14.740178121212132]
  We propose to leverage trajectory localization and prediction for unsupervised pedestrian anomaly event detection. Our proposed framework rely on the prediction errors of normal and abnormal pedestrian trajectories to detect anomalies spatially and temporally. We present experimental results on real-world benchmark datasets on varying timescales and show that our proposed trajectory-predictor-based anomaly detection pipeline is effective and efficient.
  arXiv  Detail & Related papers  (2022-07-05T19:44:34Z)
• Object-centric and memory-guided normality reconstruction for video anomaly detection [56.64792194894702]
  This paper addresses anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy. Our model learns object-centric normal patterns without seeing anomalous samples during training.
  arXiv  Detail & Related papers  (2022-03-07T19:28:39Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features [8.407188666535506]
  Most existing methods use an autoencoder to learn to reconstruct normal videos. We propose an implicit two-path AE (ITAE), a structure in which two encoders implicitly model appearance and motion features. For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features. NF models intensify ITAE performance by learning normality through implicitly learned features.
  arXiv  Detail & Related papers  (2020-10-15T05:02:02Z)
• Video Anomaly Detection for Smart Surveillance [13.447928371592557]
  Anomalies in videos are defined as events or activities that are unusual and signify irregular behavior. The goal of anomaly detection is to temporally or spatially localize the anomaly events in video sequences. This paper provides a brief overview of the recent research progress on video anomaly detection.
  arXiv  Detail & Related papers  (2020-04-01T04:13:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.