# Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videos

arXiv: https://arxiv.org/abs/2203.15437 · HTML: https://mlsanity.com/paper/2203.15437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [A Survey of Single-Scene Video Anomaly Detection](https://mlsanity.com/paper/2004.05993/) (2020)
- [Learning Deep Representations of Appearance and Motion for Anomalous Event Detection](https://mlsanity.com/paper/1510.01553/) (2015)
- [Abnormal Event Detection in Videos using Spatiotemporal Autoencoder](https://mlsanity.com/paper/1701.01546/) (2017)
- [Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes](https://mlsanity.com/paper/1609.00866/) (2016)
- [Any-Shot Sequential Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.02072/) (2020)
- [Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection](https://mlsanity.com/paper/1610.00307/) (2016)
- [Learning a distance function with a Siamese network to localize anomalies in videos](https://mlsanity.com/paper/2001.09189/) (2020)
- [Detecting abnormal events in video using Narrowed Normality Clusters](https://mlsanity.com/paper/1801.05030/) (2018)
- [Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey](https://mlsanity.com/paper/1901.08292/) (2019)
- [Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection](https://mlsanity.com/paper/2110.09768/) (2021)
- [UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance](https://mlsanity.com/paper/2011.02853/) (2020)
- [UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information](https://mlsanity.com/paper/2011.14284/) (2020)
- [Spatio-temporal Video Parsing for Abnormality Detection](https://mlsanity.com/paper/1502.06235/) (2015)

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

- [An aerial color image anomaly dataset for search missions in complex forested terrain](https://mlsanity.com/paper/2507.15492/) (2025)

## Abstract (arXiv)

UAV based surveillance is gaining much interest worldwide due to its extensive applications in monitoring wildlife, urban planning, disaster management, campus security, etc. These videos are analyzed for strange/odd/anomalous patterns which are essential aspects of surveillance. But manual analysis of these videos is tedious and laborious. Hence, the development of computer-aided systems for the analysis of UAV based surveillance videos is crucial. Despite this interest, in literature, several computer aided systems are developed focusing only on CCTV based surveillance videos. These methods are designed for single scene scenarios and lack contextual knowledge which is required for multi-scene scenarios. Furthermore, the lack of standard UAV based anomaly detection datasets limits the development of these systems. In this regard, the present work aims at the development of a Computer Aided Decision support system to analyse UAV based surveillance videos. A new UAV based multi-scene anomaly detection dataset is developed with frame-level annotations for the development of computer aided systems. It holistically uses contextual, temporal and appearance features for accurate detection of anomalies. Furthermore, a new inference strategy is proposed that utilizes few anomalous samples along with normal samples to identify better decision boundaries. The proposed method is extensively evaluated on the UAV based anomaly detection dataset and performed competitively with respect to state-of-the-art methods.