Abstract
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.
Highlights
With the development of intelligent transportation systems, environmental perception is becoming increasingly significant in the fields of advanced driver assistance systems (ADAS) and autonomous vehicles [1]
It should be noted that pedestrian detection in this paper includes pedestrians and cyclists, because the state of people while cycling is similar to that of walking and, compared with the human body, the number of point clouds scanned by Lidar on the bicycle is smaller, which can be ignored
In the 0047 dataset, the vehicle was driving in the campus and the Lidar was on a mobile platform
Summary
With the development of intelligent transportation systems, environmental perception is becoming increasingly significant in the fields of advanced driver assistance systems (ADAS) and autonomous vehicles [1]. Considering that there is a large number of traffic accidents every year, pedestrian detection has become an urgent challenge in need of a solution. Millions of people in the world are killed or injured by traffic accidents every year Effective pedestrian detection would reduce traffic accidents to protect pedestrians from vehicle injuries. Detecting the position and movement of a pedestrian has a great effect on the motion planning of autonomous vehicles
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