Abstract
The driving environment is complex and dynamic, and the attention of the driver is continuously challenged, therefore computer based assistance achieved by processing image and sensor data may increase traffic safety. While active sensors and stereovision have the advantage of obtaining 3D data directly, monocular vision is easy to set up, and can benefit from the increasing computational power of smart mobile devices, and from the fact that almost all of them come with an embedded camera. Several driving assistance application are available for mobile devices, but they are mostly targeted for simple scenarios and a limited range of obstacle shapes and poses. This paper presents a technique for generic, shape independent real-time obstacle detection for mobile devices, based on a dynamic, free form 3D representation of the environment: the particle based occupancy grid. Images acquired in real time from the smart mobile device’s camera are processed by removing the perspective effect and segmenting the resulted bird-eye view image to identify candidate obstacle areas, which are then used to update the occupancy grid. The occupancy grid tracked cells are grouped into obstacles depicted as cuboids having position, size, orientation and speed. The easy to set up system is able to reliably detect most obstacles in urban traffic, and its measurement accuracy is comparable to a stereovision system.
Highlights
Driving is a process of continuous sensing, processing the sensory information, making decisions and putting these decisions into action
While most of the research and development effort has been dedicated to adding intelligent sensing capabilities to the vehicles themselves, especially to the higher end ones, driving assistance applications on mobile devices are a less costly alternative
Facilitated by the rapid evolution of the processing power of the smart mobile devices, and by the increasingly complex and accurate sensors that come embedded into these devices, driving assistance based on image and signal processing on mobile devices may quickly become a reliable tool for traffic safety
Summary
Driving is a process of continuous sensing, processing the sensory information, making decisions and putting these decisions into action. Some parts of this loop can be done by humans, and some by machines. Humans are well equipped for sensing, but they are not perfect This is why, for decades, researchers have tried to devise better sensors, and better and faster algorithms, to help perceive the environment, help the driver and increase traffic safety. This paper presents a method for perceiving the obstacles from the dynamic driving environment based on on-board, real time processing of the images from the camera of a smart mobile device (smartphone, and tablet) that can be fitted behind the windshield of a car. No assumption about the nature or shape of the obstacle is made, and detected obstacles that are the system’s result will have position, size, speed and orientation assigned to them
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
