Abstract
More and more scholars are committed to light detection and ranging (LiDAR) as a roadside sensor to obtain traffic flow data. Filtering and clustering are common methods to extract pedestrians and vehicles from point clouds. This kind of method ignores the impact of environmental information on traffic. The segmentation process is a crucial part of detailed scene understanding, which could be especially helpful for locating, recognizing, and classifying objects in certain scenarios. However, there are few studies on the segmentation of low-channel (16 channels in this paper) roadside 3D LiDAR. This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR. The proposed method can be divided into two parts: the instance segmentation part and semantic segmentation part. The part of the instance segmentation of point cloud is based on the regional growth method, and we proposed a seed point generation method for low-channel LiDAR data. Furthermore, we optimized the instance segmentation effect under occlusion. The part of semantic segmentation of a point cloud is realized by classifying and labeling the objects obtained by instance segmentation. For labeling static objects, we represented and classified a certain object through the related features derived from its slices. For labeling moving objects, we proposed a recurrent neural network (RNN)-based model, of which the accuracy could be up to 98.7%. The result implies that the slice-based method can obtain a good segmentation effect and the slice has good potential for point cloud segmentation.
Highlights
Within the Intelligent Transportation System (ITS), traffic perception which obtains the real-time traffic flow parameters by sensors, is the base of ITS application in the areas of traffic safety analysis and prevention, traffic surveillance, and traffic control and management
This paper presents an instance and semantic segmentation method for low-channel roadside light detection and ranging (LiDAR)
We proposed a regional growth method based on slice units
Summary
Within the Intelligent Transportation System (ITS), traffic perception which obtains the real-time traffic flow parameters by sensors, is the base of ITS application in the areas of traffic safety analysis and prevention, traffic surveillance, and traffic control and management. The learning-based segmentation methods can extract and transform the features of point cloud data automatically by constructing a learning model. In this kind of algorithm, there is usually a scoring function to measure the segmentation effect of the model, and there is an optimizer to continuously make the scoring function obtain the optimal value as to achieve the best segmentation effect. The selection of seed points in the above methods is based on high density point cloud data, which is not suitable for low-channel LiDAR data. This paper proposed a novel approach to instance and semantic segmentation of low-channel roadside LiDAR point cloud. We divided all objects into road users (pedestrians and vehicles), poles, buildings, and plants to realize the semantic segmentation of a raw point cloud after instance segmentation.
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