mlsanityData to 7 Oct 2026

Online Anomaly Detection over Live Social Video Streaming

arXiv 2401.08615 ↗First posted 1 Dec 2023cs.CVcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 1 Dec 2023.

Builds on

18 of 41 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  4. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  5. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,067
  6. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  9. 2016Learning Temporal Regularity in Video Sequencescited by 254
  10. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  11. 2018Harmonious Attention Network for Person Re-Identificationcited by 211
  12. 2018Efficient GAN-Based Anomaly Detectioncited by 122
6 more
  1. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  2. 2020Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Eventscited by 70
  3. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  4. 2020Any-Shot Sequential Anomaly Detection in Surveillance Videoscited by 34
  5. 2019Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagerycited by 11
  6. 2019Online Social Media Recommendation over Streamscited by 4

Built on by

Not cited yet.