RGB cameras, while widely used for human detection and pose estimation, face challenges in real-world applications due to sensitivity to illumination changes, low-light conditions, and motion blur. Moreover, deploying state-of-the-art models is hindered by privacy concerns and the high computational demands of RGB images, making them less suitable for real-time, resource-constrained environments. This paper investigates depth and event-based cameras as alternatives to RGB for human detection and pose estimation. These modalities offer advantages such as improved low-light performance and reduced privacy concerns. Event cameras, which capture pixel-level intensity changes at high temporal resolution, minimize motion blur and consume significantly less power and memory than RGB cameras, making them ideal for edge applications. We simulate depth and event-based data using the MS COCO dataset and retrain state-of-the-art models. Results show depth data performs similarly to RGB, while event-based data, though slightly less accurate, remains competitive. These findings highlight the potential of depth and event-based cameras for efficient, privacy-preserving human detection and pose estimation in real-world applications.