# A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

arXiv: https://arxiv.org/abs/1809.03336 · HTML: https://mlsanity.com/paper/1809.03336/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Data-driven Science and Machine Learning Methods in Laser-Plasma Physics](https://mlsanity.com/paper/2212.00026/) (2022)
- [MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings](https://mlsanity.com/paper/2110.03302/) (2021)
- [Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation](https://mlsanity.com/paper/2112.09775/) (2021)

## Abstract (arXiv)

Detecting faces and heads appearing in video feeds are challenging tasks in real-world video surveillance applications due to variations in appearance, occlusions and complex backgrounds. Recently, several CNN architectures have been proposed to increase the accuracy of detectors, although their computational complexity can be an issue, especially for real-time applications, where faces and heads must be detected live using high-resolution cameras. This paper compares the accuracy and complexity of state-of-the-art CNN architectures that are suitable for face and head detection. Single pass and region-based architectures are reviewed and compared empirically to baseline techniques according to accuracy and to time and memory complexity on images from several challenging datasets. The viability of these architectures is analyzed with real-time video surveillance applications in mind. Results suggest that, although CNN architectures can achieve a very high level of accuracy compared to traditional detectors, their computational cost can represent a limitation for many practical real-time applications.