OnQ Blog

Visual perception: What happens when devices can see? [VIDEO]

May 4, 2015

Qualcomm products mentioned within this post are offered by Qualcomm Technologies, Inc. and/or its subsidiaries.

Close your eyes. The world is a very different place. The old cliché that a picture is worth a thousand words to humans is actually true, if not an understatement. Vision provides so much context. What if a picture was worth a thousand words to your smartphone and other devices? Right now, the camera on our smartphone captures a high-quality image, but what if it could understand the image and not just see it as millions of individual pixels? What if it could recognize objects, figure out context, draw conclusions, and then use that information to take an action (like your camera automatically changing settings to take the perfect picture for a given scene)? That’s where we are heading as Qualcomm brings cognitive technologies to life. And visual perception is a key ingredient.

Visual perception will unlock many new possibilities for our devices. Your smartphone will make travel easier through augmented reality by describing landmarks around you and translating street signs written in a foreign language. Drones will be able to autonomously explore and inspect unsafe areas, instead of humans. Robots will be able to assist us in our daily tasks, whether it is cleaning the house or organizing the garage. And autonomous cars will be able to independently drive themselves, identifying hazards, signs, traffic signals, and pedestrians, making our commuting lives easier and more productive.

So what’s the best approach for visual perception? Deep learning-based approaches are repeatedly demonstrating state-of-the-art results in visual perception tasks. Deep learning networks have been primarily run in the cloud, where server farms with almost unlimited compute resources have the luxury of being plugged into the wall. However, cloud-based visual perception is simply not feasible for the requirements of some of the mobile device applications I described above. Would you really want your car relying on the cloud when it needs to make a split second decision to avoid an accident? On-device deep learning is required for low latency and reliability. The big question is how you can run the compute-intensive deep learning networks within the power, thermal, memory bandwidth, and compute constraints of the mobile environment.

To find out the answer, be sure to check out Jeff Gehlhaar’s (Vice President, Qualcomm Research, a division of Qualcomm Technologies, Inc.) upcoming presentation about "Deep-learning-based Visual Perception in Mobile and Embedded Devices: Opportunities and Challenges" at the Embedded Vision Summit on May 12th. He’ll discuss innovative approaches Qualcomm Research has taken to make it possible to efficiently run deep-neural networks on Qualcomm Snapdragon processors. We’ll also be showing live demos of on-device deep learning in the exhibit area.

Also, watch for future blogs and sign up for our newsletter to receive the latest information about mobile computing.

Qualcomm Snapdragon processors are products of Qualcomm Technologies, Inc.

 

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries ("Qualcomm"). Qualcomm products mentioned within this post are offered by Qualcomm Technologies, Inc. and/or its subsidiaries. The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.

Pat Lawlor

Staff Manager, Technical Marketing

Related News

©2020 Qualcomm Technologies, Inc. and/or its affiliated companies.

References to "Qualcomm" may mean Qualcomm Incorporated, or subsidiaries or business units within the Qualcomm corporate structure, as applicable.

Qualcomm Incorporated includes Qualcomm's licensing business, QTL, and the vast majority of its patent portfolio. Qualcomm Technologies, Inc., a wholly-owned subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of Qualcomm's engineering, research and development functions, and substantially all of its products and services businesses. Qualcomm products referenced on this page are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

Materials that are as of a specific date, including but not limited to press releases, presentations, blog posts and webcasts, may have been superseded by subsequent events or disclosures.

Nothing in these materials is an offer to sell any of the components or devices referenced herein.