Abstract
Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) were manually marked for training and testing the PNN algorithm. The data were collected at three different sites representing different scenarios. The performance of the classification was evaluated by comparing the result of the PNN with those of the support vector machine (SVM) and the random forest (RF). The comparison results showed that the PNN can provide the results of classification with the highest accuracy among the three investigated methods. The overall accuracy of the PNN for object classification was 97.6% using the testing database. The errors in the classification results were also diagnosed. Discussions about the direction of future studies were also provided at the end of this paper.
Highlights
Object classification can provide numerous benefits for different transportation areas.In general, classification is defined as classifying the objects into one of the finite sets of classes
This paper developed a shape-based method with the probabilistic neural network (PNN) classifier for object classification using the roadside Light Detection and Ranging (LiDAR) data
Though they claimed a correct classification rate of 90% can be achieved in the test, this simple rule-based algorithm can only work for a pre-defined zone, and the error went high when there were multiple vehicles existing in the scene
Summary
Object classification can provide numerous benefits for different transportation areas. The auto-toll system calculates the fees based on the vehicle classification results. The typical classification procedure can be usually chopped into the following steps: Defining the number of classes, selecting the features used for classification, and applying a proper classifier for classification. Popular than the unsupervised classifier (usually rule-based method) with the development of machine learning [1]. The roadside Light Detection and Ranging (LiDAR) can provide three-dimensional shape information for the detected object, which is an emerging method for object classification serving different applications [4,5,6]. This paper developed a shape-based method with the probabilistic neural network (PNN) classifier for object classification using the roadside LiDAR data.
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