Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions
This is an incremental review paper summarizing recent advances for researchers in computer vision.
The paper reviews deep learning methods for crowd anomaly detection in smart cities, finding that pre-trained convolutional models' heterogeneities have negligible impact on performance.
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.