Pedestrian detection method using a multilayer laserscanner: Application in urban environment

Gidel, S.; Checchin, Paul; Blanc, Christophe; Chateau, Thierry; Trassoudaine, Laurent

doi:10.1109/iros.2008.4650700

Cited by 32 publications

(22 citation statements)

References 21 publications

(28 reference statements)

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“…The first principled learning approach has been taken by Arras et al [8] where a classifier for 2D point clouds has been learned by boosting a set of geometric and statistical features. As there is a natural performance limit when using only a single layer of 2D range data, several authors have been using multiple co-planar 2D laser scanners [9], [10], [11]. In the field of people detection in 3D data, Navarro et al…”

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confidence: 99%

Tracking people in 3D using a bottom-up top-down detector

Spinello

Luber

Arras

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-People detection and tracking is a key component for robots and autonomous vehicles in human environments. While prior work mainly employed image or 2D range data for this task, in this paper, we address the problem using 3D range data. In our approach, a top-down classifier selects hypotheses from a bottom-up detector, both based on sets of boosted features. The bottom-up detector learns a layered person model from a bank of specialized classifiers for different height levels of people that collectively vote into a continuous space. Modes in this space represent detection candidates that each postulate a segmentation hypothesis of the data. In the top-down step, the candidates are classified using features that are computed in voxels of a boosted volume tessellation. We learn the optimal volume tessellation as it enables the method to stably deal with sparsely sampled and articulated objects. We then combine the detector with tracking in 3D for which we take a multi-target multi-hypothesis tracking approach. The method neither needs a ground plane assumption nor relies on background learning.The results from experiments in populated urban environments demonstrate 3D tracking and highly robust people detection up to 20 m with equal error rates of at least 93%. I. IPeople detection and tracking is a key skill for mobile robots and intelligent cars in populated environments. While most of the related work in this area used vision for this task, range sensing is a particularly interesting sensor modality due to its accuracy, large field of view and robustness with respect to illumination changes and vibrations, the latter points being of particular relevance for mobile observers.In this paper we address two problems, detecting people in 3D range data and tracking people in 3D space. We extend our previous work on 3D people detection [1] by the tracking stage and an additional top-down procedure in the detection pipeline. This procedure aims at reducing false positives that typically occur with sparsely sampled individuals at large distances from the sensor. We further combine detection with tracking and present results from a tracker this is able to estimate the motion state of multiple people in 3D. To this end, we employ a multi-hypothesis tracking approach (MHT) by Reid [2] and Cox et al. [3]. In the experiments we compare our approach with related techniques for detection in 3D range data, in particular spin images [4] and templatebased classification.While there is little related work for people detection and tracking in 3D, many researchers addressed this task using 2D range data. In early works [5], [6], [7], people are detected using ad-hoc classifiers, looking for moving local minima in the scan. The first principled learning approach has been taken by Arras et al. [8] where a classifier for 2D point clouds has been learned by boosting a set of geometric and statistical features. As there is a natural performance limit when using only a single layer of 2D range data, several authors ha...

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Tracking people in 3D using a bottom-up top-down detector

Spinello

Luber

Arras

2011

2011 IEEE International Conference on Robotics and Automation

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“…Several researches using single laser scanner sensors [9], [10] and some others using multiple laser sensors [11], [12]. Meanwhile, the research of [8] used far infrared sensors that can detect objects in low resolution (long distance) as well as color and texture are less clear.…”

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confidence: 99%

A Survey of Pedestrian Detection in Video

Solichin¹,

Harjoko²,

Putra³

2014

ijacsa

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Abstract-Pedestrian detection is one of the important topics in computer vision with key applications in various fields of human life such as intelligent vehicles, surveillance and advanced robotics. In recent years, research related to pedestrian detection commonplace. This paper aims to review the papers related to pedestrian detection in order to provide an overview of the recent research. Main contribution of this paper is to provide a general overview of pedestrian detection process that is viewed from different sides of the discussion. We divide the discussion into three stages: input, process and output. This paper does not make a selection or technique best method and optimal because the best technique depends on the needs, concerns and existing environment. However, this paper is useful for future researchers who want to know the current researches related to pedestrian detection.

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“…Multi-layered detection systems have been developed recently [13,14]. Gidel et al [13] used a 4 layered sensor for pedestrian detection: the IBEO ALASCA XT scanning in 4 planes simultaneously, spaced over a vertical FOV of 3.2°.…”

Section: Introductionmentioning

confidence: 99%

“…Gidel et al [13] used a 4 layered sensor for pedestrian detection: the IBEO ALASCA XT scanning in 4 planes simultaneously, spaced over a vertical FOV of 3.2°. Martínez Mozos et al [14] uses 3 layers of 2D laser scanners to simultaneously detect different sets of features for a human: head, chest and legs.…”

Section: Introductionmentioning

confidence: 99%

“…The in-sensor layered system in Gidel et al [13] has a narrow field-of-view so the vertical distance between layers depend on the distance from the sensor, constraining the extraction of different sets of features. On the contrary, our system uses a parallel arrangement of layers (vertically separated), and scan data is read and processed in parallel.…”

Section: Introductionmentioning

confidence: 99%

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Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot

Carballo

Ohya

Yuta

2011

Int J of Soc Robotics

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Reliable people detection is an important task in several areas like security, intelligent environments and human robot interaction. People detection does not depend only upon separation of static environment objects from those showing motion (hopefully humans), a reliable system should be able to detect static people even in cluttered environments.This work presents a reliable approach for people detection and position estimation using multiple layers of Laser Range Finders (LRF) on a mobile robot. Each layer combines two LRF sensors to scan around the robot's surroundings and are vertically separated to detect distinct parts of the human body. By using AdaBoost we create strong classifiers to detect body parts, candidate segments in each layer are fused for people detection, and we use simple data association to estimate their positions. Additionally, this work introduces laser reflection intensity as a novel property for people detection. First, we present a study of laser intensity and textiles, then introduce new intensity-based features for detection, and propose a method for segment separation using laser intensity. We provide a thorough evaluation of our multi-layered system though several experiments on a mobile robot.

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Pedestrian detection method using a multilayer laserscanner: Application in urban environment

Cited by 32 publications

References 21 publications

Tracking people in 3D using a bottom-up top-down detector

Tracking people in 3D using a bottom-up top-down detector

A Survey of Pedestrian Detection in Video

Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot

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