Developing Computer Vision Algorithms for Networked Cameras

Tuesday, May 22, 2:50 PM - 3:20 PM
Summit Track: 
Technical Insights I
Mission City Ballroom B2-B5

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, we will explain how we have developed and optimized production-quality video analytics algorithms for computer vision applications.


Dukhwan Kim

Software Architect, Intel

Dukhwan Kim is a software architect for computer vision in Intel IoTG (Internet of Things Group). His main job is to drive productization of computer vision algorithms mainly for security cameras by setting requirements and building roadmaps. He has been working in the computer vision domain for more than 10 years with a broad range of experience including algorithm engineering and optimization, customer engagement and developing frameworks such as OpenCL/OpenVX and applications.

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