mlsanityData to 14 Oct 2026

Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions

arXiv 2210.13927 ↗First posted 25 Oct 2022cs.CV cs.AIcited by 3

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Abstract (arXiv)

Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. A plethora of deep learning methods have been proposed that generally outperform other machine learning solutions. Our review primarily discusses algorithms that were published in mainstream conferences and journals between 2020 and 2022. We present datasets that are typically used for benchmarking, produce a taxonomy of the developed algorithms, and discuss and compare their performances. Our main findings are that the heterogeneities of pre-trained convolutional models have a negligible impact on crowd video anomaly detection performance. We conclude our discussion with fruitful directions for future research.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 37 of its 452 references that are in the map. First posted on arXiv 25 Oct 2022.

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37 of 452 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  2. 2023Visual Crowd Analysis: Open Research Problemscited by 2
  3. 2024Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performancecited by 0