This paper presents a component-based person detection system designed to identify frontal, rear, and near-side views of people, as well as partially occluded individuals in cluttered scenes. The system's motivation stems from the need to enhance performance on frontal and rear views and to address the challenge of detecting partially occluded or body parts blending with the background. The data classification is handled by a two-layer architecture known as Adaptive Combination of Classifiers (ACC), using Support Vector Machine (SVM) classifiers. The system performs well, even when not all components of a person are detected, outperforming a full-body person detector designed similarly. The approach leverages geometric constraints and multi-scale representations to improve accuracy, making it robust to variations in lighting, orientation, and occlusion. The paper also discusses the experimental setup and results, comparing the system's performance with other component-based and full-body detection systems, demonstrating its effectiveness in various challenging scenarios.This paper presents a component-based person detection system designed to identify frontal, rear, and near-side views of people, as well as partially occluded individuals in cluttered scenes. The system's motivation stems from the need to enhance performance on frontal and rear views and to address the challenge of detecting partially occluded or body parts blending with the background. The data classification is handled by a two-layer architecture known as Adaptive Combination of Classifiers (ACC), using Support Vector Machine (SVM) classifiers. The system performs well, even when not all components of a person are detected, outperforming a full-body person detector designed similarly. The approach leverages geometric constraints and multi-scale representations to improve accuracy, making it robust to variations in lighting, orientation, and occlusion. The paper also discusses the experimental setup and results, comparing the system's performance with other component-based and full-body detection systems, demonstrating its effectiveness in various challenging scenarios.