By Antonio M. López,Atsushi Imiya,Tomas Pajdla,Jose M. Álvarez
A unified view of using desktop imaginative and prescient know-how for various different types of vehicles
Computer imaginative and prescient in motor vehicle Technology specializes in machine imaginative and prescient as on-board know-how, bringing jointly fields of study the place computing device imaginative and prescient is steadily penetrating: the automobile region, unmanned aerial and underwater cars. It additionally serves as a reference for researchers of present advancements and demanding situations in components of the appliance of desktop imaginative and prescient, concerning autos akin to complex driving force counsel (pedestrian detection, lane departure caution, site visitors signal recognition), self reliant riding and robotic navigation (with visible simultaneous localization and mapping) or unmanned aerial autos (obstacle avoidance, panorama type and mapping, hearth probability assessment).
The total position of machine imaginative and prescient for the navigation of alternative automobiles, in addition to expertise to deal with on-board functions, is analysed.
- Presents the most recent advances within the box of desktop imaginative and prescient and car applied sciences in a hugely informative and comprehensible method, together with the elemental arithmetic for every problem.
- Provides a entire precis of the state-of-the-art laptop imaginative and prescient ideas in autos from the navigation and the addressable purposes issues of view.
- Offers an in depth description of the open demanding situations and company possibilities for the instant destiny within the field of regard established motor vehicle technologies.
This is vital examining for laptop imaginative and prescient researchers, in addition to engineers operating in automobile applied sciences, and scholars of computing device vision.
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Additional resources for Computer Vision in Vehicle Technology: Land, Sea, and Air
Computer Vision in Vehicle Technology: Land, Sea, and Air by Antonio M. López,Atsushi Imiya,Tomas Pajdla,Jose M. Álvarez