Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods
This addresses the need for accurate object recognition in autonomous driving, but appears incremental as it applies existing machine learning methods to a standard task in the field.
The paper tackled the problem of classifying LiDAR pointcloud data into vehicles, pedestrians, and bikers for self-driving cars, presenting a real-time object detection system using a LiDAR-based detector and neural network classifier.
The goal of this paper is to classify objects mapped by LiDAR sensor into different classes such as vehicles, pedestrians and bikers. Utilizing a LiDAR-based object detector and Neural Networks-based classifier, a novel real-time object detection is presented essentially with respect to aid self-driving vehicles in recognizing and classifying other objects encountered in the course of driving and proceed accordingly. We discuss our work using machine learning methods to tackle a common high-level problem found in machine learning applications for self-driving cars: the classification of pointcloud data obtained from a 3D LiDAR sensor.