Adar Paz, Imaging and Computer Vision Team Leader at CEVA, presents the "Challenges in Object Detection on Embedded Devices" tutorial at the May 2014 Embedded Vision Summit.

As more products ship with integrated cameras, there is an increased potential for computer vision (CV) to enable innovation. For instance, CV can tackle the "scene understanding" problem by first figuring out what the various objects in the scene are. Such "object detection" capability holds big promise for embedded devices in mobile, automotive, and surveillance markets. However, performing real-time object detection while meeting a strict power budget remains a challenge on existing processors.

In this session, Paz analyzes the trade-offs of various object detection, feature extraction and feature matching algorithms, their suitability for embedded vision processing, and recommends methods for efficient implementation in a power- and budget-constrained embedded device.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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