Technical Insights

“Low-power Computer Vision: Status, Challenges and Opportunities,” a Presentation from Purdue University

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Professor Yung-Hsiang Lu of Purdue University presents the "Low-power Computer Vision: Status, Challenges and Opportunities" tutorial at the May 2019 Embedded Vision Summit. Energy efficiency plays a crucial role in making computer vision successful in battery-powered systems, including drones, mobile phones and autonomous robots. Since 2015, IEEE has been organizing an annual competition on low-power […]

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“Optimizing SSD Object Detection for Low-power Devices,” a Presentation from Allegro

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Moses Guttmann, CTO and founder of Allegro, presents the "Optimizing SSD Object Detection for Low-power Devices" tutorial at the May 2019 Embedded Vision Summit. Deep learning-based computer vision models have gained traction in applications requiring object detection, thanks to their accuracy and flexibility. For deployment on low-power hardware, single-shot detection (SSD) models are attractive due

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“Using Deep Learning for Video Event Detection on a Compute Budget,” a Presentation from PathPartner Technology

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Praveen Nayak, Tech Lead at PathPartner Technology, presents the "Using Deep Learning for Video Event Detection on a Compute Budget" tutorial at the May 2019 Embedded Vision Summit. Convolutional neural networks (CNNs) have made tremendous strides in object detection and recognition in recent years. However, extending the CNN approach to understanding of video or volumetric

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“Can Simulation Solve the Training Data Problem?,” a Presentation from Mindtech

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Peter McGuinness, Vice President of AI and Services at Mindtech, presents the "Can Simulation Solve the Training Data Problem?" tutorial at the May 2019 Embedded Vision Summit. While there has been rapid progress in the adoption of neural networks and the evolution of neural network structures, the problem of training data remains. Even companies with

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“Mining Site Data Extraction Using 3D Machine Learning,” a Presentation from Strayos

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Ravi Sahu, Founder and CEO of Strayos, presents the "Mining Site Data Extraction Using 3D Machine Learning" tutorial at the May 2019 Embedded Vision Summit. This talk focuses on extracting invariant features for segmentation of 3D models of mining sites. The image data is generated by stitching together geo-tagged images from a drone. The 3D

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“APIs for Accelerating Vision and Inferencing: An Industry Overview of Options and Trade-offs,” a Presentation from the Khronos Group

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Neil Trevett, President of the Khronos Group and Vice President at NVIDIA, presents the "APIs for Accelerating Vision and Inferencing: An Industry Overview of Options and Trade-offs" tutorial at the May 2019 Embedded Vision Summit. The landscape of SDKs, APIs and file formats for accelerating inferencing and vision applications continues to evolve rapidly. Low-level compute

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“Object Trackers: Approaches and Applications,” a Presentation from Intel

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Minje Park, Deep Learning R&D Engineer at Intel, presents the "Object Trackers: Approaches and Applications" tutorial at the May 2019 Embedded Vision Summit. Object tracking is a powerful algorithm component and one of the fundamental building blocks for many real-world computer vision applications. Object trackers provide two main benefits when incorporated into a localization module.

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“Selecting and Exploiting Sensors for Sensor Fusion in Consumer Robots,” a Presentation from Daniel Casner

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Daniel Casner, formerly a systems engineer at Anki, presents the "Selecting and Exploiting Sensors for Sensor Fusion in Consumer Robots" tutorial at the May 2019 Embedded Vision Summit. How do you design robots that are aware of their unstructured environments at a consumer price point? Excellent sensing is required but using low cost sensors is

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“Performance Analysis for Optimizing Embedded Deep Learning Inference Software,” a Presentation from Arm

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Gian Marco Iodice, Staff Compute Performance Software Engineer at Arm, presents the "Performance Analysis for Optimizing Embedded Deep Learning Inference Software" tutorial at the May 2019 Embedded Vision Summit. Deep learning on embedded devices is currently enjoying significant success in a number of vision applications—particularly smartphones, where increasingly prevalent AI cameras are able to enhance

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“Methods for Creating Efficient Convolutional Neural Networks,” a Presentation from Xnor.ai

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Mohammad Rastegari, Chief Technology Officer at Xnor.ai, presents the "Methods for Creating Efficient Convolutional Neural Networks" tutorial at the May 2019 Embedded Vision Summit. In the past few years, convolutional neural networks (CNNs) have revolutionized several application domains in AI and computer vision. The biggest challenge with state-of-the-art CNNs is the massive compute demands that

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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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