Deep Convolutional Neural Networks for pedestrian detection |
| |
| Affiliation: | 1. The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China;2. The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China;3. Wisconsin Institute for Discovery, University of Wisconsin–Madison, Madison, 53715, USA;1. LRDSI Laboratory, Saad Dahlab University – Blida1, Blida, Algeria;2. Department of Computer Science and Engineering, University of Quebec in Outaouais, Gatineau, QC, Canada J8X 3X7;1. School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China;2. National Key Laboratory of Science and Technology on Space Microwave, Xi’an 710000, China;3. Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium;1. Moulay Ismail University of Meknes, Meknes, Morocco;2. Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, United States |
| |
| Abstract: | Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular Convolutional Neural Networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection and segmentation, often outperforming the previous gold standards by a large margin. In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time. Finally, we tested the system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a forerunner computational brain of future self-driving cars. |
| |
| Keywords: | Deep learning Pedestrian detection Convolutional Neural Networks Optimization |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
