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Dukhwan Kim, computer vision software architect at Intel, presents the “Developing Computer Vision Algorithms for Networked Cameras” tutorial at the May 2018 Embedded Vision Summit.

Video analytics is one of the key elements in network cameras. Computer vision capabilities such as pedestrian detection, face detection and recognition and object detection and tracking are necessary for effective video analysis. With recent advances in deep learning technology, many developers are now utilizing deep learning to implement these capabilities.

However, developing a deep learning algorithm requires more than just training models using Caffe or TensorFlow. It should start from an understanding of use cases, which affect the nature of required training dataset, and should be tightly bound with the hardware platform to get the best performance. In this presentation, Kim explains how Intel has developed and optimized production-quality video analytics algorithms for computer vision applications.

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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