Authors:
Taiki Yamamoto
1
;
Fumito Shinmura
2
;
Daisuke Deguchi
3
;
Yasutomo Kawanishi
1
;
Ichiro Ide
1
and
Hiroshi Murase
1
Affiliations:
1
Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi and Japan
;
2
Institutes of Innovation for Future Society, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi and Japan
;
3
Information Strategy Office, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi and Japan
Keyword(s):
Active-scan LIDAR, Stochastic Sampling, Pedestrian Detection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Shape Representation and Matching
Abstract:
In recent years, LIDAR is playing an important role as a sensor for understanding environments of a vehicle’s surroundings. Active-scan LIDAR is being actively developed as a LIDAR that can control the laser irradiation direction arbitrary and rapidly. In comparison with conventional uniform-scan LIDAR (e.g. Velodyne HDL-64e), Active-scan LIDAR enables us to densely scan even distant pedestrians. In addition, if appropriately controlled, this sensor has a potential to reduce unnecessary laser irradiations towards non-target objects. Although there are some preliminary studies on pedestrian scanning strategy for Active-scan LIDARs, in the best of our knowledge, an efficient method has not been realized yet. Therefore, this paper proposes a novel pedestrian scanning method based on orientation aware pedestrian likelihood estimation using the orientation-wise pedestrian’s shape models with local distribution of measured points. To evaluate the effectiveness of the proposed method, we co
nducted experiments by simulating Active-scan LIDAR using point-clouds from the KITTI dataset. Experimental results showed that the proposed method outperforms the conventional methods.
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