Heads Up, Down Under: Sydney Suburb Enhances Livability with Traffic Analytics

With a new university campus nearby and an airport under construction, the city of Liverpool, Australia, 27 kilometers southwest of Sydney, is growing fast.

More than 30,000 people are expected to make a daily commute to its central business district. Liverpool needed to know the possible impact to traffic flow and movement of pedestrians, cyclists and vehicles.

The city already hosts closed-circuit televisions to monitor safety and security. Each CCTV captures lots of video and data that, due to stringent privacy regulations, is mainly combed through after an incident has been reported.

The challenge before the city was to turn this massive dataset into information that could help it run more efficiently, handle an influx of commuters and keep the place liveable for residents — without compromising anyone’s privacy.

To achieve this goal, the city has partnered with the Digital Living Lab of the University of Wollongong. Part of Wollongong’s SMART Infrastructure Facility, the DLL has developed what it calls the Versatile Intelligent Video Analytics platform. VIVA, for short, unlocks data so that owners of CCTV networks can access real-time, privacy-compliant data to make better informed decisions.

VIVA is designed to convert existing infrastructure into edge-computing devices embedded with the latest AI. The platform’s state-of-the-art deep learning algorithms are developed at DLL on the NVIDIA Metropolis platform. Their video analytics deep-learning models are trained using transfer learning to adapt to use cases, optimized via NVIDIA TensorRT software and deployed on NVIDIA Jetson edge AI computers.

“We designed VIVA to process video feeds as close as possible to the source, which is the camera,” said Johan Barthelemy, lecturer at the SMART Infrastructure Facility of the University of Wollongong. “Once a frame has been analyzed using a deep neural network, the outcome is transmitted and the current frame is discarded.”

Disposing of frames maintains privacy as no images are transmitted. It also reduces the bandwidth needed.

Beyond city streets like in Liverpool, VIVA has been adapted for a wide variety of applications, such as identifying and tracking wildlife; detecting culvert blockage for stormwater management and flash flood early warnings; and tracking of people using thermal cameras to understand people’s mobility behavior during heat waves. It can also distinguish between firefighters searching a building and other building occupants, helping identify those who may need help to evacuate.

Making Sense of Traffic Patterns

The research collaboration between SMART, Liverpool’s city council and its industry partners is intended to improve the efficiency, effectiveness and accessibility of a range of government services and facilities.

For pedestrians, the project aims to understand where they’re going, their preferred routes and which areas are congested. For cyclists, it’s about the routes they use and ways to improve bicycle usage. For vehicles, understanding movement and traffic patterns, where they stop, and where they park are key.

Understanding mobility within a city formerly required a fleet of costly and fixed sensors, according to Barthelemy. Different models were needed to count specific types of traffic, and manual processes were used to understand how different types of traffic interacted with each other.

Using computer vision on the NVIDIA Jetson TX2 at the edge, the VIVA platform can count the different types of traffic and capture their trajectory and speed. Data is gathered using the city’s existing CCTV network, eliminating the need to invest in additional sensors.

Patterns of movements and points of congestion are identified and predicted to help improve street and footpath layout and connectivity, traffic management and guided pathways. The data has been invaluable in helping Liverpool plan for the urban design and traffic management of its central business district.

Machine Learning Application Built Using NVIDIA Technologies

SMART trained the machine learning applications on its VIVA platform for Liverpool on four workstations powered by a variety of NVIDIA TITAN GPUs, as well as six workstations equipped with NVIDIA RTX GPUs to generate synthetic data and run experiments.

In addition to using open databases such as OpenImage, COCO and Pascal VOC for training, DLL created synthetic data via an in-house application based on the Unity Engine. Synthetic data allows the project to learn from numerous scenarios that might not otherwise be present at any given time, like rainstorms or masses of cyclists.

“This synthetic data generation allowed us to generate 35,000-plus images per scenario of interest under different weather, time of day and lighting conditions,” said Barthelemy. “The synthetic data generation uses ray tracing to improve the realism of the generated images.”

Inferencing is done with NVIDIA Jetson Nano, NVIDIA Jetson TX2 and NVIDIA Jetson Xavier NX, depending on the use case and processing required.

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Sand Safety: Startup’s Lifeguard AI Hits the Beach to Save Lives

A team in Israel is making a splash with AI.

It started as biz school buddies Netanel Eliav and Adam Bismut were looking to solve a problem to change the world. The problem found them: Bismut visited the Dead Sea after a drowning and noticed a lack of tech for lifeguards, who scanned the area with age-old binoculars.

The two aspiring entrepreneurs — recent MBA graduates of Ben-Gurion University, in the country’s south — decided this was their problem to solve with AI.

“I have two little girls, and as a father, I know the feeling that parents have when their children are near the water,” said Eliav, the company’s CEO.

They founded Sightbit in 2018 with BGU classmates Gadi Kovler and Minna Shezaf to help lifeguards see dangerous conditions and prevent drownings.

The startup is seed funded by Cactus Capital, the venture arm of their alma mater.

Sightbit is now in pilot testing at Palmachim Beach, a popular escape for sunbathers and surfers in the Palmachim Kibbutz area along the Mediterranean Sea, south of Tel Aviv. The sand dune-lined destination, with its inviting, warm aquamarine waters, gets packed with thousands of daily summer visitors.

But it’s also a place known for deadly rip currents.

Danger Detectors

Sightbit has developed image detection to help spot dangers to aid lifeguards in their work. In collaboration with the Israel Nature and Parks Authority, the Beersheba-based startup has installed three cameras that feed data into a single NVIDIA Jetson AGX at the lifeguard towers at Palmachim beach. NVIDIA Metropolis is deployed for video analytics.

The system of danger detectors enables lifeguards to keep tabs on a computer monitor that flags potential safety concerns while they scan the beach.

Sightbit has developed models based on convolutional neural networks and image detection to provide lifeguards views of potential dangers. Kovler, the company’s CTO, has trained the company’s danger detectors on tens of thousands of images, processed with NVIDIA GPUs in the cloud.

Training on the images wasn’t easy with sun glare on the ocean, weather conditions, crowds of people, and people partially submerged in the ocean, said Shezaf, the company’s CMO.

But Sightbit’s deep learning and proprietary algorithms have enabled it to identify children alone as well as clusters of people. This allows its system to flag children who have strayed from the pack.

Rip Current Recognition

The system also harnesses optical flow algorithms to detect dangerous rip currents in the ocean for helping lifeguards keep people out of those zones.  These algorithms make it possible to identify the speed of every object in an image, using partial differential equations to calculate acceleration vectors of every voxel in the image.

Lifeguards can get updates on ocean conditions so when they start work they have a sense of hazards present that day.

“We spoke with many lifeguards. The lifeguard is trying to avoid the next accident. Many people go too deep and get caught in the rip currents,” said Eliav.

Cameras at lifeguard towers processed on the single compact supercomputing Jetson Xavier and accessing Metropolis can offer split-second inference for alerts, tracking, statistics and risk analysis in real time.

The Israel Nature and Parks Authority is planning to have a structure built on the beach to house more cameras for automated safety, according to Sightbit.

COVID-19 Calls 

Palmachim Beach lifeguards have a lot to watch, especially now as people get out of their homes for fresh air after the region begins reopening from COVID-19-related closures.

As part of Sightbit’s beach safety developments, the company had been training its network to spot how far apart people were to help gauge child safety.

This work also directly applies to monitoring social distancing and has attracted the attention of potential customers seeking ways to slow the spread of COVID-19. The Sightbit platform can provide them crowding alerts when a public area is overcrowded and proximity alerts for when individuals are too close to each other, said Shezaf.

The startup has put in extra hours to work with those interested in its tech to help monitor areas for ways to reduce the spread of the pathogen.

“If you want to change the world, you need to do something that is going to affect people immediately without any focus on profit,” said Eliav.

 

Sightbit is a member of NVIDIA Inception, a virtual accelerator program that helps startups in AI and data science get to market faster.

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Floating on Creativity: SuperBlimp Speeds Rendering Workflows with NVIDIA RTX GPUs

Rendering is a critical part of the design workflow. But as audiences and clients expect ever higher-quality graphics, agencies and studios must tap into the latest technology to keep up with rendering needs.

SuperBlimp, a creative production studio based just outside of London, knew there had to be a better way to achieve the highest levels of quality in the least amount of time. They’re leaving CPU rendering behind and moving to NVIDIA RTX GPUs, bringing significant acceleration to the rendering workflows for their unique productions.

After migrating to full GPU rendering, SuperBlimp experienced accelerated render times, making it easier to complete more iterations on their projects and develop creative visuals faster than before.

Blimping Ahead of Rendering With RTX

Because SuperBlimp is a small production studio, they needed the best performance at a low cost, so they turned to NVIDIA GeForce RTX 2080 Ti GPUs.

SuperBlimp had been using NVIDIA GPUs for the past few years, so they were already familiar with the power and performance of GPU acceleration. But they always had one foot in the CPU camp and needed to constantly switch between CPU and GPU rendering.

However, CPU render farms required too much storage space and took too much time. When SuperBlimp finally embraced full GPU rendering, they found RTX GPUs delivered the level of computing power they needed to create 3D graphics and animations on their laptops at a much quicker rate.

Powered by NVIDIA Turing, the most advanced GPU architecture for creators, RTX GPUs provide dedicated ray-tracing cores to help users speed up rendering performance and produce stunning visuals with photorealistic details.

And with NVIDIA Studio Drivers, the artists at SuperBlimp are achieving the best performance on their creative applications. NVIDIA Studio Drivers undergo extensive testing against multi-app creator workflows and multiple revisions of top creative applications, including Adobe Creative Cloud, Autodesk and more.

For one of their recent projects, an award-winning short film titled Playgrounds, SuperBlimp used Autodesk Maya for 3D modeling and Chaos Group’s V-Ray GPU software for rendering. V-Ray enabled the artists to create details that helped produce realistic surfaces, from metallic finishes to plastic materials.

“With NVIDIA GPUs, we saw render times reduce from 3 hours to 15 minutes. This puts us a great position to create compelling work,” said Antonio Milo, director at SuperBlimp. “GPU rendering opened the door for a tiny studio like us to design and produce even more eye-catching content than before.”

Image courtesy of SuperBlimp.

Now, SuperBlimp renders their projects using NVIDIA GeForce RTX 2080 Ti and GTX 1080 Ti GPUs to bring incredible speeds for rendering, so their artists can complete creative projects with the powerful, flexible and high-quality performance they need.

Learn how NVIDIA GPUs are powering the future of creativity.

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