# Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection

arXiv: https://arxiv.org/abs/1610.00307 · HTML: https://mlsanity.com/paper/1610.00307/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2016.

## Built on by (most cited first, 30 of 32)

- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection](https://mlsanity.com/paper/2111.09099/) (2021)
- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection](https://mlsanity.com/paper/2111.08644/) (2021)
- [Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild](https://mlsanity.com/paper/2203.03800/) (2022)
- [Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2303.12369/) (2023)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection](https://mlsanity.com/paper/2207.08003/) (2022)
- [Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors](https://mlsanity.com/paper/2306.12041/) (2023)
- [Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection](https://mlsanity.com/paper/2209.12148/) (2022)
- [DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network](https://mlsanity.com/paper/2211.00882/) (2022)
- [Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement](https://mlsanity.com/paper/2108.01975/) (2021)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [EVAL: Explainable Video Anomaly Localization](https://mlsanity.com/paper/2212.07900/) (2022)
- [Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection](https://mlsanity.com/paper/2302.07106/) (2023)
- [FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation](https://mlsanity.com/paper/2106.08613/) (2021)
- [Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies](https://mlsanity.com/paper/2203.13716/) (2022)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [A Modular and Unified Framework for Detecting and Localizing Video Anomalies](https://mlsanity.com/paper/2103.11299/) (2021)
- [SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model](https://mlsanity.com/paper/2504.10320/) (2025)
- [Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies](https://mlsanity.com/paper/2103.10502/) (2021)
- [Spatio-temporal predictive tasks for abnormal event detection in videos](https://mlsanity.com/paper/2210.15741/) (2022)
- [Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation](https://mlsanity.com/paper/2211.15597/) (2022)
- [Multimedia Datasets for Anomaly Detection: A Review](https://mlsanity.com/paper/2112.05410/) (2021)
- [Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests](https://mlsanity.com/paper/2108.02356/) (2021)
- [Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videos](https://mlsanity.com/paper/2203.15437/) (2022)
- [Adversarial Attention for Human Motion Synthesis](https://mlsanity.com/paper/2204.11751/) (2022)
- [Beyond the Known: Adversarial Autoencoders in Novelty Detection](https://mlsanity.com/paper/2404.04456/) (2024)
- ["Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks](https://mlsanity.com/paper/2104.01478/) (2021)
- [Generalized One-Class Learning Using Pairs of Complementary Classifiers](https://mlsanity.com/paper/2106.13272/) (2021)

## Abstract (arXiv)

Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features. In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality. We specifically propose a novel measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from existing CNN models) with low-level Optical-Flow. One of the advantage of this method is that it can be used without the fine-tuning costs. The proposed method is validated on challenging abnormality detection datasets and the results show the superiority of our method compared to the state-of-the-art methods.