Autonomous driving of a personal mobility vehicle such as a wheelchair in a walking space is crucial as a means of transportation for the elderly and the physically handicapped. To realize this, accurate pedestrian detection is indispensable. As existing 3D object detection methods are trained with a roadway dataset, they are widely used for object detection in roadways. These methods have two major drawbacks as regards the detection of objects in walking spaces. The first is that they largely depend on the different LIDAR models. To eliminate this issue, we propose a 3D object detection method, CosPointPillars, that does not take the reflection intensities of the LIDAR point cloud, which causes a sensor model dependency, as input. The second drawback is that networks trained with a roadway dataset cannot sufficiently detect pedestrians (who are major traffic participants in walking spaces) located within a short distance; this is because the roadway dataset hardly includes nearby pedestrians. To solve this issue, we generated a new walking space dataset called SimDataset, which includes nearby pedestrians as a training dataset in the simulations. An experiment on a real walking space showed that SimDataset is suitable for use in pedestrian detection.

Sensor-independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation

**Session Chair**: prof. Aliaksandr Siarohin

PS T3.4: Computer Vision, Detection and Applications

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Takahiro Shimizu

1

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Sensor-independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation

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