Object-Level Unknown Obstacle Detection

This paper presents a novel method for object-level unknown obstacle detection in driving scenes that reduces false positives. The proposed method combines existing anomaly detectors, depth estimation, and object detection techniques to achieve object-level predictions. Our method can predict anomalies as bound-box instance detections. These bounding boxes can then be used to refine anomaly detection by suppressing false positives outside of the bounding boxes. The proposed method has several advantages, including object-level detections that are more practical than pixel-level detections, and the ability to find and refine region proposals for obstacle detection. The paper provides a detailed explanation of all components of the system and includes an ablation study on the usage of depth estimation, as well as execution time averages on different hardware. The proposed method is evaluated using different metrics and benchmarks, demonstrating the effectiveness and relevance of the existing proposed methods. Overall, our proposed method has the potential to significantly improve object-level anomaly detection making it suitable for real-world applications.