# TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance

arXiv: https://arxiv.org/abs/2411.11003 · HTML: https://mlsanity.com/paper/2411.11003/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 17 Nov 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision](https://mlsanity.com/paper/2007.04687/) (2020)
- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Anomaly Detection in Video via Self-Supervised and Multi-Task Learning](https://mlsanity.com/paper/2011.07491/) (2020)
- [Multiview Transformers for Video Recognition](https://mlsanity.com/paper/2201.04288/) (2022)
- [A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction](https://mlsanity.com/paper/2108.06852/) (2021)
- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2211.15098/) (2022)
- [GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection](https://mlsanity.com/paper/1908.05884/) (2019)
- [Localizing Anomalies from Weakly-Labeled Videos](https://mlsanity.com/paper/2008.08944/) (2020)
- [Motion-Aware Feature for Improved Video Anomaly Detection](https://mlsanity.com/paper/1907.10211/) (2019)
- [Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video](https://mlsanity.com/paper/2108.03825/) (2021)
- [Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection](https://mlsanity.com/paper/2107.13720/) (2021)
- [Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2105.14737/) (2021)
- [Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection](https://mlsanity.com/paper/2203.12840/) (2022)
- [BatchNorm-based Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2311.15367/) (2023)
- [Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detection](https://mlsanity.com/paper/2206.08568/) (2022)
- [Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos](https://mlsanity.com/paper/2202.06503/) (2022)
- [Anomaly detection in surveillance videos using transformer based attention model](https://mlsanity.com/paper/2206.01524/) (2022)
- [Detection of Object Throwing Behavior in Surveillance Videos](https://mlsanity.com/paper/2403.06552/) (2024)

## Built on by (most cited first, 1 of 1)

- [M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detection](https://mlsanity.com/paper/2511.05564/) (2025)

## Abstract (arXiv)

Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications in learning the temporal dynamics of specific events. This paper presents a temporal-granularity method for an anomaly detection model (TeG) in real-world surveillance, combining spatio-temporal features at different time-scales. The TeG model employs multi-head cross-attention blocks and multi-head self-attention blocks for this purpose. Additionally, we extend the UCF-Crime dataset with new anomaly types relevant to Smart City research project. The TeG model is deployed and validated in a city surveillance system, achieving successful real-time results in industrial settings.