Decision-Level Error Attribution and Depth-Aware Safety Evaluation of Monocular RGB Human Tracking for Autonomous Robot Navigation
Abdulahi Taiwo, Chijioke Cyriacus Ekechi
Monocular RGB tracking is useful for robots operating near people, but perception errors affect binary stop/proceed decisions in ways that geometric accuracy metrics do not capture. We examine how much decision error arises from the safety rule versus perception. We prove that lateral-only distance is a lower bound on planar distance: with accurate positions, it cannot miss dangers identified by the planar rule, but it produces unnecessary stops. We introduce a two-factor Shapley attribution separating rule and perception contributions, and further distinguish missed detections from localization errors. Experiments use ETH and UCY pedestrian videos with simulated stationary robots, disjoint calibration and evaluation pedestrians, and time-block bootstrap intervals. Across 898 pedestrians in three scenes, the lateral rule missed no dangers on annotated positions but achieved only 5.6-31.9% stop precision. In two scenes with RGB perception, calibrated ground-plane localization achieved 0.59 m and 0.51 m planar RMSE, compared with 1.72 m and 1.39 m for a geometric baseline and 0.81 m and 0.68 m for calibrated Depth Anything V2. Detection recall of 0.43 and 0.26 made missed detections the leading source of missed danger. A two-standard-deviation safety margin with ground-plane localization matched or improved on the lateral rule's missed-danger rate in all eight scene-threshold settings and reduced unnecessary stops in seven. All robot interactions are simulated, and outcomes are offline decision classifications, not demonstrations of collision avoidance.
尚无 AI 解读，展示原文摘要