In the field of intelligent transportation systems, pedestrian detection has become a problem that is urgently in need of a solution. Effective pedestrian detection reduces accidents and protects pedestrians from injuries. A pedestrian-detection algorithm, namely, single template matching with kernel density estimation clustering (STM-KDE), is proposed in this paper. First, the KDE-based clustering method is utilized to extract candidate pedestrians in point clouds. Next, the coordinates of the point clouds are transformed into the pedestrians’ local coordinate system and projection images are generated. Locally adaptive regression kernel features are extracted from the projection image and matched with the template features by using cosine similarity, based on which pedestrians are distinguished from other columnar objects. Finally, comparative experiments using KITTI datasets are conducted to verify pedestrian-detection performance. Compared with the STM with radially bounded nearest neighbor (STM-RBNN) algorithm and the KDE-based pedestrian-detection algorithm, the proposed algorithm can segment gathering pedestrians and distinguish them from other columnar objects in real scenarios.
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