# Online Anomaly Detection over Live Social Video Streaming

arXiv: https://arxiv.org/abs/2401.08615 · HTML: https://mlsanity.com/paper/2401.08615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events](https://mlsanity.com/paper/2008.11988/) (2020)
- [Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge](https://mlsanity.com/paper/1709.09121/) (2017)
- [Any-Shot Sequential Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.02072/) (2020)
- [Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery](https://mlsanity.com/paper/1904.05304/) (2019)
- [Online Social Media Recommendation over Streams](https://mlsanity.com/paper/1901.01003/) (2019)

## Abstract (arXiv)

Social video anomaly is an observation in video streams that does not conform to a common pattern of dataset's behaviour. Social video anomaly detection plays a critical role in applications from e-commerce to e-learning. Traditionally, anomaly detection techniques are applied to find anomalies in video broadcasting. However, they neglect the live social video streams which contain interactive talk, speech, or lecture with audience. In this paper, we propose a generic framework for effectively online detecting Anomalies Over social Video LIve Streaming (AOVLIS). Specifically, we propose a novel deep neural network model called Coupling Long Short-Term Memory (CLSTM) that adaptively captures the history behaviours of the presenters and audience, and their mutual interactions to predict their behaviour at next time point over streams. Then we well integrate the CLSTM with a decoder layer, and propose a new reconstruction error-based scoring function REIA to calculate the anomaly score of each video segment for anomaly detection. After that, we propose a novel model update scheme that incrementally maintains CLSTM and decoder. Moreover, we design a novel upper bound and ADaptive Optimisation Strategy (ADOS) for improving the efficiency of our solution. Extensive experiments are conducted to prove the superiority of AOVLIS.