A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications
This is an incremental comparison study for video surveillance practitioners, highlighting trade-offs between accuracy and speed.
The paper compared state-of-the-art CNN architectures for face and head detection in real-time video surveillance, finding that while CNNs achieve high accuracy, their computational cost limits practical use.
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.