论文标题
深人群异常检测：最先进的，挑战和未来的研究方向
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions
论文作者
论文摘要
在智能城市的背景下，人群异常检测是计算机视觉中最受欢迎的主题之一。已经提出了大量深度学习方法，通常超过其他机器学习解决方案。我们的综述主要讨论了2020年至2022年之间在主流会议和期刊上发表的算法。我们介绍了通常用于基准测试的数据集，对已发达算法产生分类法，并讨论和比较其性能。我们的主要发现是，预训练的卷积模型的异质性对人群视频异常检测性能有忽略的影响。我们以富有成果的未来研究的方向结束了讨论。
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.
