Using Diverse Neural Networks for Safer Human Pose Estimation: Towards Making Neural Networks Know When They Don’t Know
Patrick Schlosser, Christoph Ledermann · 2020
In recent years, human pose estimation has seen great improvements by the use of neural networks. However, these approaches are unsuitable for safety-critical applications such as human-robot interaction (HRI), as no guarantees are given whether a produced detection is correct or not and false detections with high confidence scores are produced on a regular basis. In this work, we propose a method to identify and eliminate false detections by comparing keypoint detections from different neural networks and assigning a 'Don't know' label in the case of a mismatch. Our approach is driven by the principle of software diversity, a technique recommended by the safety standard IEC 61508-7 [1] for dealing with software implementation faults. We evaluate our general concept on the MPII human pose dataset [2] using available ground truth data to calculate a suitable threshold for our keypoint comparison, reducing the number of false detections by approx. 61%. For the application at runtime, where no ground truth data is available, we introduce a method to calculate the needed threshold directly from keypoint detections. In further experiments, it was possible to reduce the number of false detections by approx. 75%. Eliminating keypoints by comparison also lowers the correct detection rate, which we maintained above 75% in all experiments. As this effect is limited and non-critical regarding safety we believe that the proposed approach can lead the way to a safe use of neural networks for human pose estimation in the future.