In recent years, the detection and localization of tiny persons have garnered significant attention due to their critical applications in various surveillance and security scenarios. Traditional multi-modal methods predominantly rely on well-registered image pairs, necessitating the use of sophisticated sensors and extensive manual effort for registration, which restricts their practical utility in dynamic, real-world environments. Addressing this gap, this paper introduces a novel non-registered multi-modal benchmark named NRPerson, specifically designed to advance the field of tiny person detection and localization by accommodating the complexities of real-world scenarios. The NRPerson dataset comprises 8548 RGB-IR image pairs, meticulously collected and filtered from 22 video sequences, enriched with 889,207 high-quality annotations that have been manually verified for accuracy. Utilizing NRPerson, we evaluate several leading detection and localization models across both mono-modal and non-registered multi-modal frameworks. Furthermore, we develop a comprehensive set of natural multi-modal baselines for the innovative non-registered track, aiming to enhance the detection and localization of unregistered multi-modal data using a cohesive and generalized approach. This benchmark is poised to facilitate significant strides in the practical deployment of detection and localization technologies by mitigating the reliance on stringent registration requirements.