Context-compensated Probabilistic Pedestrian Detection

Abstract

Autonomous vehicles are equipped with a wide range of sensors and corresponding object detectors tuned to reliably detect vulnerable road users in a variety of conditions. However, the activation scores of these object detectors are easily influenced by contextual factors. To address this challenge, we propose a probabilistic, sensor-agnostic and context-adaptive calibration layer that translates the activation scores of the candidate detections into likelihood ratios that are tuned to that specific context. Our method, seamlessly integrated with the underlying object detector, effectively enhances detection precision by mitigating contextual biases. As a proof of concept, we demonstrate that calibrating the activation scores for four pre-trained state-of-the-art detectors achieves an average precision improvement of up to 4% on the Waymo open dataset for the specific task of pedestrian detection using regular RGB cameras. In challenging scenarios, the average precision can improve up to 9%. Additionally, we showcase that context-calibration emerges as a viable alternative to conventional transfer learning when dealing with limited datasets.