VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection

Detection of fallen persons due to, for example, health problems, violence, or accidents, is a critical challenge. Accordingly, detection of these anomalous events is of paramount importance for a number of applications, including but not limited to CCTV surveillance, security, and health care. Given that many detection systems rely on a comprehensive and diverse dataset, it is crucial to include fallen person images collected under diverse environments and various situations. However, existing datasets are limited to only specific environmental conditions and lack diversity. To address the challenges mentioned above and help researchers develop more robust detection systems, we create a novel, large-scale dataset for the detection of fallen persons composed of fallen person images collected in various real-world scenarios, with the support of the South Korean government. Our Vision-based Fallen Person (VFP290K) dataset consists of 294,713 frames of fallen persons extracted from 178 videos, including 131 scenes in 49 locations. We empirically demonstrate the effectiveness of the features through extensive experiments analyzing the performance shift based on object detection models. In addition, we evaluate our VFP290K dataset with properly divided versions of our dataset by measuring the performance of fallen person detecting systems. Furthermore, we discuss limitations and future work that could be extended based on our dataset. We ranked first in the first round of the anomalous behavior recognition track of AI Grand Challenge 2020, South Korea, using our VFP290K dataset, which can be found here³. Our achievement implies the usefulness of our dataset for research on fallen person detection, which can further extend to other applications, such as intelligent CCTV or monitoring systems.