Abstract:Heart rate is an important physiological parameter for measuring human cardiovascular health and emotional stress. However, video-based non-contact heart rate detection techniques can degrade the detection accuracy in real scenarios due to facial movements and lighting changes. To solve the problem, this study proposes a new method of heart rate detection based on adaptive superpixel segmentation and multi-region integrated analysis depending on the high correlation between the selection of the region of interest (ROI) in a heart rate detection algorithm and its detection accuracy. Firstly, a face detection and tracking algorithm is used to crop the face image. Then the ROI is divided into non-overlapping sub-blocks by an adaptive superpixel segmentation algorithm. The original blood volume pulse matrix of each sub-block is constructed by chromaticity feature extraction. Finally, the pulse matrix is analyzed using multiple indicators, and the best region is selected for heart rate estimation. The experimental results show that the heart rate detection accuracy can be effectively improved by adaptive superpixel segmentation and optimal selection through multi-region analysis. The accuracy reaches 99.1% and 95.6% under stationary and motion disturbance conditions, respectively, and the accuracy is improved by up to 8.2% under illumination disturbance conditions compared with that of the traditional method. The proposed method enhances the robustness of heart rate detection in real scenarios.