# Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling

arXiv: https://arxiv.org/abs/2307.04159 · HTML: https://mlsanity.com/paper/2307.04159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation](https://mlsanity.com/paper/2104.14812/) (2021)
- [Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities](https://mlsanity.com/paper/1811.00648/) (2018)
- [Background Subtraction in Real Applications: Challenges, Current Models and Future Directions](https://mlsanity.com/paper/1901.03577/) (2019)
- [Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation](https://mlsanity.com/paper/1811.05255/) (2018)
- [BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos](https://mlsanity.com/paper/1907.11371/) (2019)
- [Unsupervised Change Detection Based on Image Reconstruction Loss](https://mlsanity.com/paper/2204.01200/) (2022)
- [Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding](https://mlsanity.com/paper/1801.02225/) (2018)
- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [Real-Time Semantic Background Subtraction](https://mlsanity.com/paper/2002.04993/) (2020)
- [A Deep Convolutional Neural Network for Background Subtraction](https://mlsanity.com/paper/1702.01731/) (2017)
- [G-LBM:Generative Low-dimensional Background Model Estimation from Video Sequences](https://mlsanity.com/paper/2003.07335/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection](https://mlsanity.com/paper/2507.18513/) (2025)

## Abstract (arXiv)

Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to make them practical is to reduce their false alarm rate. In this work, we develop a method-agnostic weakly supervised a-contrario validation process, based on high dimensional statistical modeling of deep features, to reduce the number of false alarms of any change detection algorithm. We also raise the insufficiency of the conventionally used pixel-wise evaluation, as it fails to precisely capture the performance needs of most real applications. For this reason, we complement pixel-wise metrics with object-wise metrics and evaluate the impact of our approach at both pixel and object levels, on six methods and several sequences from different datasets. Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.