Error Propagation Analysis in Multi-Person Fall Detection: A System-Level Perspective

초록

Fall detection in multi-person environments, such as nursing homes and rehabilitation centers, is essential for ensuring the safety of vulnerable populations. Despite advances in deep learning, current vision- and skeleton-based fall detection systems often exhibit false negatives and reduced reliability in real-world scenarios due to scene complexity. This study presents a system-level analysis of fall detection errors by comparing four approaches-two skeleton-based methods using ST-GCN and ProtoGCN, a rule-based method, and a VIRA-GCN-based 3D joint method-on 95 RGB video sequences captured under minimally constrained multi-person conditions. We define six error types: skeleton structural interference, localized joint recognition failure, temporal skeleton identity inconsistency, object-to-skeleton association failure, viewpoint-induced observation limitation, and action-level ambiguity with similar activities. Although most methods achieved high event-level recall, their false-positive and false-negative patterns differed. The rule-based approach showed the most balanced performance, under the present experimental conditions, whereas the ST-GCN-based skeleton approach was more sensitive to joint-level and tracking instability. ProtoGCN reduced false positives but increased false negatives, showing a more conservative decision pattern. The VIRA-GCN-based 3D joint approach provided spatial cues but did not eliminate upstream pose and tracking errors. These results highlight the need for skeleton-depth fusion, robust identity tracking, occlusion handling, and enhanced joint recognition in real-world multi-person fall detection.

키워드