While variety of object detection datasets exist, few address the specific and practical challenges faced by blind and visually impaired persons (VIPs) in real-world outdoor mobility contexts. Existing datasets typically focus on general-purpose objects or indoor scenes and overlook dynamic and environmental factors that critically affect safer mobility in daily life. To address this gap, we introduce VIP-Mobility360 — a novel, primary annotated dataset uniquely designed to support AI models that enhance outdoor mobility for VIPs. VIP-Mobility360 is the first dataset curated in direct response to real-world obstacle reports collected based on Royal National Institute of Blind People (RNIB) reports, consultations, and global case studies involving hazards such as e-scooters, bollards, wheelie bins, construction barriers, and pavement cracks. The dataset contains high-resolution video recordings (100 sequences per object class) captured entirely in outdoor environments, incorporating diverse weather conditions, background variations, scale, orientation, and 360° camera perspectives. This design ensures data realism, robustness, and applicability to real-time tracking, geometric understanding, and time-series forecasting. All instances are manually annotated to support supervised learning, object detection, depth estimation, and semantic segmentation. Beyond assistive technology, VIP-Mobility360 has broader relevance in areas such as autonomous vehicles (e.g., puddle and hazard detection), generative AI for geometric reconstruction, and urban planning for inclusive design. Moreover, by advancing the development of AI-driven smart canes, wearable mobility aids, and context-aware guidance systems, this dataset contributes to reducing reliance on costly personal carers or guide dogs—offering a socially equitable and economically viable solution to independent living for the blind community. This dataset therefore represents a transformative step toward building trustworthy, inclusive, and ethically aligned AI applications in real-world healthcare and urban mobility contexts.