キャリブレーション済みカメラを用いた2D距離データにおける自己教師あり人物検出
Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera
キャリブレーション済みカメラの画像検出器から疑似ラベルを自動生成し、2D LiDAR人物検出器を自己教師ありで学習・微調整する手法を提案。手動注釈なしで検出性能を向上できる。
著者: Dan Jia, Mats Steinweg, Alexander Hermans, Bastian Leibe
分類: cs.CV, cs.RO
原文アブストラクト
Deep learning is the essential building block of state-of-the-art person detectors in 2D range data. However, only a few annotated datasets are available for training and testing these deep networks, potentially limiting their performance when deployed in new environments or with different LiDAR models. We propose a method, which uses bounding boxes from an image-based detector (e.g. Faster R-CNN) on a calibrated camera to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors. Through experiments on the JackRabbot dataset with two detector models, DROW3 and DR-SPAAM, we show that self-supervised detectors, trained or fine-tuned with pseudo-labels, outperform detectors trained only on a different dataset. Combined with robust training techniques, the self-supervised detectors reach a performance close to the ones trained using manual annotations of the target dataset. Our method is an effective way to improve person detectors during deployment without any additional labeling effort, and we release our source code to support relevant robotic applications.
