Robust, Resilient Enhanced CAMSHIFT Model: Advancing Face Detection and Tracking Stability in Challenging Environments

Abstract

This paper presents the development of a robust CAMSHIFT model for theoretical face detection and tracking. The proposed model integrates innovative techniques such as Perceptual Grouping, three Connected Component Operators, Weighted Adaptive Colour Histogram, and Selective Adaptation. Experimental results highlight its superior performance across scenarios like occlusions, varying illumination, near/far face tracking, skin-like background tracking, and disturbance from multiple faces. The normalized log-likelihood index serves as a robust indicator for face tracking analysis. Connected Component operations provide strong markers for error detection in video sequences. The enhanced CAMSHIFT algorithm exhibits resilience and stability, even in the presence of occlusions. Comparisons with the original CAMSHIFT reveal the enhanced model's superiority, extending tracking range to 500 cm, a calculated enhancement of 42.9 percent improvement. The study consistently favours the robust and resilient CAMSHIFT model in tracking against skin-like backgrounds and disturbances. Despite webcam convenience in used for algorithm development, the benefits of high-performance camera systems are envisioned for future research. This model is a significant advancement in face detection methods, promising improved adaptability and tracking capabilities.

Keywords

• CAMSHIFT
• Perceptual grouping
• Connected Components
• Weighted adaptive histogram
• Selective adaptation