Real-time AdaBoost cascade face tracker based on likelihood map and optical flow

TL;DR

This paper introduces a real-time face tracking method combining likelihood maps and optical flow with an adapted Viola Jones detector, improving detection stability and occlusion handling on standard laptops.

Contribution

It presents a novel face tracking algorithm that integrates likelihood maps and optical flow into a modified Viola Jones framework for enhanced real-time performance.

Findings

Outperforms standard Viola Jones in detection rate and stability

Handles occlusions effectively

Achieves comparable accuracy to CNN-based detectors with less computation

Abstract

The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered. In addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. The authors also evaluate two recently published face detectors based on convolutional networks and deformable part models with their algorithm showing a comparable accuracy at a fraction of the computation time.