Detection Level and Target Level Road User Classification with Radar Point Cloud
2023 IEEE Sensors Applications Symposium (SAS)(2023)
摘要
This paper examines the classification of vulnerable road users and vehicles using radar point-cloud data at two tracking levels. While automotive radar offers numerous capabilities suitable for real-world tracking scenarios, it faces challenges due to sparse detection. Consequently, a clustering algorithm could fail to detect a target's cluster due to a limited number of detections. Moreover, it can divide a single object cluster into multiple clusters or merge distinct entities into a single cluster, impacting the accuracy of target-level (cluster-level) classification. On the other hand, the detection-level classification assigns class labels to individual radar detections, which can also be employed without clustering but requires a higher computational complexity. By extracting different levels of information from radar data, this study analyzes detection-level and target-level classification using various combinations of extracted features and classifiers. The performance of the proposed approaches in terms of classification accuracy and computation time for testing is evaluated using a publicly available dataset for automotive radar applications.
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关键词
Point cloud,automotive radar,clustering,feature extraction,feature selection,classification
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