Pierrick Letort

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In this paper we focus on improving the performance of people detection algorithm on fish-eye images in a safety system for heavy machines. Fish-eye images give the advantage of a very wide angle-of-view, which is important in the context of heavy machines. However, the distortions in fish-eye images present many difficulties for image processing. The(More)
In this paper we focus on the evaluation of the deformable part model (DPM) proposed by Felzenszwalb et al. [IO] in the context of vision-based people detection in heavy machines applications. The proposed system uses a single fisheye camera to provide a wide field-of-view (FOV) at low cost. However, the fisheye optical distortions present several(More)
In this paper, we propose a multi-sensors system to detect people in the context of construction sites using heavy machines. The system includes a LIght Detection And Ranging (Lidar) sensor and a fisheye camera. We present an effective method to determine regions of interest (ROI) on fisheye images using Lidar data, which can be used with different sensors(More)
This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye(More)
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