カメラとLiDARを融合した自動運転車向け歩行者検出・追跡フレームワーク
A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data
LiDARを深度画像に変換しRGB画像と組み合わせた深層学習で歩行者を検出し、カルマンフィルタとオプティカルフローで追跡する統合手法を提案した。
著者: Muhammad Mobaidul Islam, Abdullah Al Redwan Newaz, Ali Karimoddini
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection.
