Fast Pedestrian Detection in Dense Environment with a Laser Scanner and a Camera

Gate, Gwennael; Breheret, Amaury; Nashashibi, Fawzi

doi:10.1109/vetecs.2009.5073555

Cited by 17 publications

(7 citation statements)

References 16 publications

(17 reference statements)

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“…While some related algorithms have been introduced in [4], [6], few of them are suitable to autonomous vehicles, with considerations to the demanding working environment. An algorithm similar to our approach is proposed in [3]. It depends on a four-layer LIDAR to track pedestrians and do preliminary classification, and then use camera to refine the classification belief.…”

Section: Campus Testbedmentioning

confidence: 99%

Autonomous personal vehicle for the first- and last-mile transportation services

Chong

Qin

Bandyopadhyay

et al. 2011

2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS)

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Abstract-This paper describes an autonomous vehicle testbed that aims at providing the first-and last-mile transportation services. The vehicle mainly operates in a crowded urban environment whose features can be extracted a priori. To ensure that the system is economically feasible, we take a minimalistic approach and exploit prior knowledge of the environment and the availability of the existing infrastructure such as cellular networks and traffic cameras. We present three main components of the system: pedestrian detection, localization (even in the presence of tall buildings) and navigation. The performance of each component is evaluated. Finally, we describe the role of the existing infrastructural sensors and show the improved performance of the system when they are utilized.

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Section: Campus Testbedmentioning

confidence: 99%

Autonomous personal vehicle for the first- and last-mile transportation services

Chong

Qin

Bandyopadhyay

et al. 2011

2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS)

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“…Our method shares similar ideas as in [2,5,7,12,14]: using the depth information to reduce the search space in the image. In [5], the authors describe a car detection and tracking algorithm based on a single layer LiDAR.…”

Section: Lidar-based Detectionmentioning

confidence: 99%

Combining LiDAR space clustering and convolutional neural networks for pedestrian detection

Matti

Ekenel

Thiran

2017

2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Pedestrian detection is an important component for safety of autonomous vehicles, as well as for traffic and street surveillance. There are extensive benchmarks on this topic and it has been shown to be a challenging problem when applied on real use-case scenarios. In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks (CNN) and surprisingly few detection frameworks have been built upon multi-cue approaches. In this work, we develop a new pedestrian detector for autonomous vehicles that exploits LiDAR data, in addition to visual information. In the proposed approach, LiDAR data is utilized to generate region proposals by processing the three dimensional point cloud that it provides. These candidate regions are then further processed by a state-of-the-art CNN classifier that we have fine-tuned for pedestrian detection. We have extensively evaluated the proposed detection process on the KITTI dataset. The experimental results show that the proposed LiDAR space clustering approach provides a very efficient way of generating region proposals leading to higher recall rates and fewer misses for pedestrian detection. This indicates that LiDAR data can provide auxiliary information for CNN-based approaches.

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“…Although conceptually very interesting, the practical exploitation of the method is limited by the 2D nature of laser scans and by the relatively high costs of the employed sensor. On top of the usage of a 2D laser scanner, Gate et al [16] introduces a camera that aid the classification of moving objects. A purely vision-based method was proposed by Agrawal et al [1] who employ a calibrated stereo configuration of conventional RGB cameras.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Panteleris

Argyros

2015

Computer Vision - ECCV 2014 Workshops

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Abstract. The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.

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Fast Pedestrian Detection in Dense Environment with a Laser Scanner and a Camera

Cited by 17 publications

References 16 publications

Autonomous personal vehicle for the first- and last-mile transportation services

Autonomous personal vehicle for the first- and last-mile transportation services

Combining LiDAR space clustering and convolutional neural networks for pedestrian detection

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

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