Pedestrian Recognition Using High-definition LIDAR

Kidono, Kiyosumi; Watanabe, Arata; Naito, Takashi; Miura, Jun

doi:10.7210/jrsj.29.963

Cited by 40 publications

(74 citation statements)

References 16 publications

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“…The vertical boundary is set to any predetermined threshold that identifies a pedestrian in Ref. 1. In Figure 6(c), the radar RoI and the lidar raw data cluster are represented by the dash-dot rectangle and black points, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…1,2 In addition, various techniques have been applied to develop autonomous driving technologies, such as advanced driver assistance systems, advanced smart cruise control, lane keeping assist systems, and autonomous emergency braking systems. In spite of such technological advancements, pedestrian accident rates continue to increase annually.…”

Section: Introductionmentioning

confidence: 99%

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Detection scheme for a partially occluded pedestrian based on occluded depth in lidar–radar sensor fusion

et al. 2017

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Son, "Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion," Opt. Eng. 56(11), 113112 (2017), doi: 10.1117/1.OE.56.11.113112. Abstract. Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar-radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar–radar sensor fusion

et al. 2017

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“…The approach has low computational cost but its performance degrades a lot at longer distances. In Kidono et al (2011), two features were introduced to improve the classification performance. One is the slice feature, which represents the profile of a human body by widths at the different height levels.…”

Section: Introductionmentioning

confidence: 99%

Global–local articulation pattern-based pedestrian detection using 3D Lidar data

Liu

et al. 2016

Remote Sensing Letters

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Highly variable human poses and pedestrian occlusion make light detection and ranging (Lidar)-based pedestrian detection challenging. This letter proposes a novel framework to address these issues. Other than dividing humans into arbitrary number of parts and using the same features for all part detectors, we represent humans with globallocal articulated parts and formulate new features relying on each part's own character. Articulated parts are effective because they each usually maintain a relatively consistent shape across a broader range of body poses. In addition, to extract visible human segments from cluttered surroundings with the presence of pedestrian occlusion, both 3D information and 2D spatial information are used in a coarse-to-fine manner, making the interaction of human part and its neighbouring objects better analysed. The algorithm is evaluated over a busy street dataset and is shown to be competitive with the state-ofthe-art Lidar-based algorithms. Remarkably, even at long distances, up to 20 m, it can handle pedestrian occlusion efficiently and effectively. ARTICLE HISTORY

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“…Premebida [5] 의 Navaro-Serment [6] 는 각 물체에 해당하는 3D 포인트 [6] 에서 소개된 특징이고,   ,   는 Kidono의 연구 [9] 에서 제안한 특징을 인용하였다. …”

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Performance Improvement of Pedestrian Detection using a GM-PHD Filter

Lee¹,

Seo²

2015

Journal of the Institute of Electronics and Information Enginee

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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.

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Pedestrian Recognition Using High-definition LIDAR

Cited by 40 publications

References 16 publications

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar–radar sensor fusion

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar–radar sensor fusion

Global–local articulation pattern-based pedestrian detection using 3D Lidar data

Performance Improvement of Pedestrian Detection using a GM-PHD Filter

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