• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.150-157
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• 2015

Pedestrian detection has largely been researched as one of the important technologies for autonomous driving vehicle and preventing accidents. There are two categories for pedestrian detection, camera-based and LIDAR-based. LIDAR-based methods have the advantage of the wide angle of view and insensitivity of illuminance change while camera-based methods have not. However, there are several problems with 3D LIDAR, such as insufficient resolution to detect distant pedestrians and decrease in detection rate in a complex situation due to segmentation error and occlusion. In this paper, two methods using GM-PHD filter are proposed to improve the poor rates of pedestrian detection algorithms based on 3D LIDAR. First one improves detection performance and resolution of object by automatic accumulation of points in previous frames onto current objects. Second one additionally enhances the detection results by applying the GM-PHD filter which is modified in order to handle the poor situation to classified multi target. A quantitative evaluation with autonomously acquired road environment data shows the proposed methods highly increase the performance of existing pedestrian detection algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.773-777
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• 2016

In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.218-232
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• 2022

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.