The use of hand gestures can be the most intuitive human-machine interaction medium.The early approaches for hand gesture recognition used device-based methods.These methods use mechanical or optical sensors attached ...The use of hand gestures can be the most intuitive human-machine interaction medium.The early approaches for hand gesture recognition used device-based methods.These methods use mechanical or optical sensors attached to a glove or markers,which hinder the natural human-machine communication.On the other hand,vision-based methods are less restrictive and allow for a more spontaneous communication without the need of an intermediary between human and machine.Therefore,vision gesture recognition has been a popular area of research for the past thirty years.Hand gesture recognition finds its application in many areas,particularly the automotive industry where advanced automotive human-machine interface(HMI)designers are using gesture recognition to improve driver and vehicle safety.However,technology advances go beyond active/passive safety and into convenience and comfort.In this context,one of America’s big three automakers has partnered with the Centre of Pattern Analysis and Machine Intelligence(CPAMI)at the University of Waterloo to investigate expanding their product segment through machine learning to provide an increased driver convenience and comfort with the particular application of hand gesture recognition for autonomous car parking.The present paper leverages the state-of-the-art deep learning and optimization techniques to develop a vision-based multiview dynamic hand gesture recognizer for a self-parking system.We propose a 3D-CNN gesture model architecture that we train on a publicly available hand gesture database.We apply transfer learning methods to fine-tune the pre-trained gesture model on custom-made data,which significantly improves the proposed system performance in a real world environment.We adapt the architecture of end-to-end solution to expand the state-of-the-art video classifier from a single image as input(fed by monocular camera)to a Multiview 360 feed,offered by a six cameras module.Finally,we optimize the proposed solution to work on a limited resource embedded platform(Nvidia Jet展开更多
Parking is an important and indispensable skill for drivers. With rapid urban development, the automatic parking assistant system is one of the key components in future automobiles. Path planning is always essential f...Parking is an important and indispensable skill for drivers. With rapid urban development, the automatic parking assistant system is one of the key components in future automobiles. Path planning is always essential for solving parking problems. In this paper, a path planning method is proposed for parking using straight lines and circular curves of different radius without collisions with obstacles. The parking process is divided into two steps in which the vehicle reaches the goal state through the intermediate state from the initial state. The intermediate state will be selected from the intermediate state set with a certain criterion in order to avoid obstacles. Similarly, an appropriate goal state will be selected based on the size of the parking lot. In addition, an automatic parking system is built, which effectively achieves the parking lot perception, path planning and performs parking processes in the environment with obstacles. The result of simulations and experiments demonstrates the feasibility and practicality of the proposed method and the automatic parking system.展开更多
文摘The use of hand gestures can be the most intuitive human-machine interaction medium.The early approaches for hand gesture recognition used device-based methods.These methods use mechanical or optical sensors attached to a glove or markers,which hinder the natural human-machine communication.On the other hand,vision-based methods are less restrictive and allow for a more spontaneous communication without the need of an intermediary between human and machine.Therefore,vision gesture recognition has been a popular area of research for the past thirty years.Hand gesture recognition finds its application in many areas,particularly the automotive industry where advanced automotive human-machine interface(HMI)designers are using gesture recognition to improve driver and vehicle safety.However,technology advances go beyond active/passive safety and into convenience and comfort.In this context,one of America’s big three automakers has partnered with the Centre of Pattern Analysis and Machine Intelligence(CPAMI)at the University of Waterloo to investigate expanding their product segment through machine learning to provide an increased driver convenience and comfort with the particular application of hand gesture recognition for autonomous car parking.The present paper leverages the state-of-the-art deep learning and optimization techniques to develop a vision-based multiview dynamic hand gesture recognizer for a self-parking system.We propose a 3D-CNN gesture model architecture that we train on a publicly available hand gesture database.We apply transfer learning methods to fine-tune the pre-trained gesture model on custom-made data,which significantly improves the proposed system performance in a real world environment.We adapt the architecture of end-to-end solution to expand the state-of-the-art video classifier from a single image as input(fed by monocular camera)to a Multiview 360 feed,offered by a six cameras module.Finally,we optimize the proposed solution to work on a limited resource embedded platform(Nvidia Jet
基金Supported by the National Natural Science Foundation of China(61473042,61105092,61173076)Beijing Higher Education Young Elite Teacher Project(YETP1215)
文摘Parking is an important and indispensable skill for drivers. With rapid urban development, the automatic parking assistant system is one of the key components in future automobiles. Path planning is always essential for solving parking problems. In this paper, a path planning method is proposed for parking using straight lines and circular curves of different radius without collisions with obstacles. The parking process is divided into two steps in which the vehicle reaches the goal state through the intermediate state from the initial state. The intermediate state will be selected from the intermediate state set with a certain criterion in order to avoid obstacles. Similarly, an appropriate goal state will be selected based on the size of the parking lot. In addition, an automatic parking system is built, which effectively achieves the parking lot perception, path planning and performs parking processes in the environment with obstacles. The result of simulations and experiments demonstrates the feasibility and practicality of the proposed method and the automatic parking system.
