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Blog, self-driving cars

The New York Times reported on Sunday that Waymo, the self-driving unit of Google’s parent company Alphabet, and the ride-sharing startup, Lyft are teaming up to bring self-driving technology to the mainstream. “We’re looking forward to working with Lyft to explore new self-driving products that will make our roads safer and transportation more accessible. Lyft’s vision and commitment to improving the way cities move will help Waymo’s self-driving technology reach […]

The New York Times reported on Sunday  that Waymo, the self-driving unit of Google’s parent company Alphabet, and the ride-sharing startup, Lyft are teaming up to bring self-driving technology to the mainstream.

“We’re looking forward to working with Lyft to explore new self-driving products that will make our roads safer and transportation more accessible. Lyft’s vision and commitment to improving the way cities move will help Waymo’s self-driving technology reach more people in more places,” Waymo said in a statement to Recode.

Together, these companies have partnerships with the majority of major auto manufacturers. Lyft announced a partnership earlier this year with General Motors to test the Chevy Bolt with the general public within the next few years. Waymo has deals with Fiat Chrysler and Honda testing their technology on the road.

What does this mean for Uber? They have poured hundreds of millions of dollars into the development of self-driving cars to catch up to Google and view the technology as crucial to their future. Uber has had a rough year to date; this may be a considerable setback for them.

The Latest Self-Driving Technology Updates

Self-driving cars are one of the hottest things in tech right now. It feels like just yesterday we were saying “can you imagine!?” Here we are in 2017 on the cusp of having autonomous cars pick us up at our front door. To get an update on where we are, here are a few more updates on what is going on in the world of autonomous cars.

Training Datasets Released For All to Leverage

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Self-driving cars use advanced Artificial Intelligence algorithms to make thousands of decisions. To know what decisions to make, the algorithms are trained using datasets of various scenarios.

Training datasets are usually very expensive to create because it takes a lot of time to annotate the images. Annotating a single image (or a single frame from a video) can take between seconds and hours depending on complexity or, how much you are looking to teach an algorithm.

Luckily for technology startups, according to TechCrunch, Mapillary is releasing a free dataset of 25,000 street-level images from 190 countries, with pixel-level annotations that can be used to train automotive AI systems. Mapillary is a crowdsourcing company that uses computer vision to read images uploaded to their database by people around the world using smartphones to identify locations in 3D and recognizes the order of objects within them.

The release of this dataset opens new opportunity for tech startups to advance machine learning algorithms used in self-driving cars. It’s no surprise that this dataset release was sponsored by big auto manufacturers Lyft, Toyota, and Daimler.

Humans may be what is slowing down self-driving cars

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The benefits to self-driving cars are many: safer roads, less traffic, lower fuel consumption, and don’t forget enhanced human productivity – no more lost hours driving, you can now be productive on your commute. With all of these benefits for humans, it turns out that we may be the problem holding the technology back.

Driving takes a certain amount of assertiveness, according to John C. Dvorak, Columnist at PCMag.com, self-driving cars are too polite. In ‘right of way’ situations like 4-way stops, human drivers will assert their intentions to go; the autonomous car may sit until the intersection clear. If a cyclist is hogging the road, it will slow down and drive behind until the path is clear.

John Adams, a professor at University College London, says “Driving in cities would be unacceptably slow if autonomously-operating cars were required to assume that every pedestrian might jump into traffic as fast as humanly possible. But if pedestrians came to learn that cars would always avoid them then they would likely act in much less controlled ways on streets and pavements.”

Will the algorithms become more advanced to handle these situations? Or will humans have to adapt to allow for these polite road warriors?

No more fighting over parking spots

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Once a self-driving car has dropped you off, it needs to find a place to park. As a human driver, we all know how difficult and annoying this can be. A hackathon team that came out of the TechCrunch Disrupt NY event, Val.ai created a way for autonomous vehicles to bid for parking spaces in an auction.

The tech-twist here is that these cars aren’t looking for an empty parking spot, they are negotiating with other autonomous cars which are currently parked and will be leaving soon. The model was based on public parking spots which bring up concerns about using public space for private use, a term TechCrunch calls “#JerkTech.” But, there is still lots of opportunity for private parking lots.

There you have it, the latest in self-driving cars. Do you work on self-driving technology? We would love to hear from you to discuss how iMerit’s dataset services may be of use to you. Get in touch!

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Blog, computer vision

On Monday, the iMerit team will be arriving in Santa Clara, California to join the many innovators, technologists, and engineers from the computer vision industry at this year’s Embedded Vision Summit. The Embedded Vision Summit brings product and application developers, business leaders, investors, and entrepreneurs who are all focused on embedded vision together to see the latest in practical technology. Attendees get to see the latest in practical technology and […]

On Monday, the iMerit team will be arriving in Santa Clara, California to join the many innovators, technologists, and engineers from the computer vision industry at this year’s Embedded Vision Summit.

The Embedded Vision Summit brings product and application developers, business leaders, investors, and entrepreneurs who are all focused on embedded vision together to see the latest in practical technology. Attendees get to see the latest in practical technology and dataset services to bring visual intelligence into cloud applications, embedded systems, mobile apps, wearables, and PCs.

Along with showcases of the latest computer vision products and services, attendees will get to hear from industry experts in five in-depths tracks on technical insights, business, enabling technologies, and fundamentals of visual intelligence. Steven Cadavid, President and Founder of Kinatrax, will be presenting on their markerless motion capture system that computes kinematic data of an in-game baseball pitch.

Markerless Motion Capture System Captures Kinematic Data of MLB Pitchers

KinaTrax’s Markerless Motion Capture System consists of a variety of imaging devices that are mounted throughout the baseball stadium to capture the detailed movement of pitchers. They develop 3D kinematic models that are used by teams to monitor pitchers’ performance and to prevent injury. Currently, these devices are mounted at the home stadiums of the Cubs and the Tampa Bay Rays, along with another undisclosed stadium.

Computer Vision and Machine Learning algorithms are used to capture the biomechanics of a pitcher at over 300 frames per second. The video is recovered in 3D and reconstructed frame by frame, producing an image for every motion within the pitch sequence which are then annotated at 20 joint centers. KinaTrax leverages iMerit’s team of computer vision data experts to provide on-demand and scalable annotation resources from end-to-end through KinaTrax’s data analysis workflow.

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To create the 3D models, “iMerit teams process in-game footage and prepare it for analysis. We then annotate images and videos to create 3D models of the players. iMerit integrates directly with KinaTrax systems, facilitating a seamless handoff of data. In the past year, iMerit has built a precise 3D model for approximately 300 pitchers across all 30 MLB teams, covering most pitching personnel,” said Jai Natarajan, VP of Technology at iMerit.

Computer Vision Algorithms and Humans Work Together to Create Baseball’s New ‘Moneyball’

The idea of leveraging in-game data to enhance pitcher performance “will make a profound difference in major league baseball, it will change the game,” said Cadavid. Billy Beane revolutionized baseball with his analytical, evidence-based approach to selecting players dubbed ‘Moneyball’. KinaTrax’s offering is supplying team management with the data they need to make the best decisions about a pitcher’s health.

The 3D Kinematic models can be used to generate comprehensive and customizable biomechanic reports. According to Steven Cadavid, “The primary uses for it are evaluating the mechanics over time, the performance enhancement and injury prevention component. From an injury prevention, the datasets we’re collecting are really unprecedented.”

KinaTrax is taking a different approach to motion capture technology by leveraging video annotation to gain the data required to build 3D kinematic models. This means that the subjects, in this case, the pitchers, don’t need to wear markers to capture the data. This key element to KinaTrax’s technology allows them to not only capture training data but in-game data as well. Combining this with iMerit’s on-demand dataset service offering, humans in combination with technology are revolutionizing the game.  

Want to Learn More about Data-Driven Baseball?

If you are attending the Embedded Vision Summit May 1-3, be sure to check out Cadavid’s talk on Using Markerless Motion Capture to Win Baseball GamesAlso be sure to come by the iMerit booth to grab some Philz coffee. If you are unable to attend, check out this video.

Want to learn more about how iMerit can help your Computer Vision technology? Get in touch with our team today.

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