An object in motion looks fundamentally different from an object at rest — especially to a computer. To get a better idea of this concept, let’s imagine a film strip of a sprinter running: The person and pose in one frame look drastically different from the next frame, right?

Making sense of dynamic objects is taking on new importance as cities begin incorporating IoT devices like smart cameras to streamline municipal life. The town of Yuma, Arizona, is a great example of this. The city recently installed cameras on streetlights that can detect when cars, bicycles, and pedestrians travel through intersections, and it uses that data to optimise signal switching.

Athena Security is pioneering another interesting application of moving-video analysis: The company sells software that uses artificial intelligence to detect when people are fighting, fleeing, or lurking to determine whether crimes are being committed (or are imminent). Unsurprisingly, everyone from municipal police departments to Fortune 500 companies is interested in this AI application.

The applications are endless for IoT devices like smart cameras that analyse moving video. Fortunately, this technology has now reached a point where almost anything is possible.

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Solving mysteries in moving video

Using computers to analyse video isn’t exactly a new concept. However, there’s one problem hampering the development of video analysis: Moving video is full of dynamic variables that can confuse even the smartest computers.

Objects look completely different in low light compared to bright light, for instance, which can lead to false analyses. Perspective offers up another challenge: Think about how different a car looks when it’s traveling parallel and then perpendicular to a relative point.

Other issues that might be confusing for a machine’s analysis of video include moving shadows, complex backgrounds, obscured objects, unexpected movements, and a camera’s technical limitations. For all these reasons, moving-video analysis has historically had a lot of potential — but not too many practical applications.

That’s all changing with advances in deep learning systems, which are often referred to as neural networks. Today, computing has advanced to a point where systems can learn from past data to get better at understanding future data.

The ability to learn and adapt is crucial for computers that need to make sense of the ever-changing data coming from moving video — and different combinations of neural networks could provide a solution. With convolutional neural networks, for example, computers model space in three dimensions to better predict the trajectory of objects within that space.

Deep neural networks can help cancel out background images so cameras can focus explicitly on moving objects. There are also recurrent neural networks that excel at pattern recognition. Each of these networks has strengths and weaknesses, but using them in the right combination makes moving-video analysis highly accurate in almost any setting.

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Connectivity and the future of smart cameras

My company recently worked on a project that demonstrates how far connected devices like smart cameras have come, as well as the challenges they still face. For this particular project, a client in Israel asked us to develop a program to detect kicking motions in live soccer matches televised at 20 frames per second.

From the start, the project endured two obstacles: First, our team had to distinguish between an actual “kick” and a swinging leg motion that looked quite similar. Second, we needed to do that at 20 frames per second. That’s a higher resolution than most surveillance footage, and it’s packed with much more data to analyse.

Initially, we tried developing an algorithm that would create two “bounding boxes” around both a player’s foot and the soccer ball, and then register when those two boxes met. In practice, however, detecting kicks became extremely inaccurate when players were clumped together (and we know that happens a lot in soccer).

The solution? Tweak the deep learning element. We adjusted how the underlying neural networks were configured so we could accelerate object detection. Then, we created a data set using 500 frames taken from 20 seconds of a soccer match. Our team manually annotated this data to identify kicks and “non-kicks,” and we used it to “teach” our algorithms to make that distinction.

Our program eventually identified 58 per cent of real kicks; improving the numbers was possible through feeding the program data from more matches and different sets of players.

That’s because it proved that, with the right configurations, deep learning can make sense of all the complexities within the moving video within the connected device. While achieving these ends might take a ton of reference data, the technology has proved its usefulness and has finally made moving-video analysis a reality.

This kind of technology can be applied in many areas, from IoT surveillance systems to self-driving cars. And if one thing is certain, there’s not much further to go before moving-video analysis begins transforming our lives.

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The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project’s maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare.

Originally developed at the National University of Singapore, SINGA joined ASF’s incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.

The success of deep-learning models has been driven by the use of very large datasets, such as ImageNet with hundreds of thousands of images, and complex models with millions of parameters. Google’s BERT natural-language model contains 300 million parameters and is trained on nearly 3 billion words. However, this training often requires hours, if not days, to complete. To speed up this process, researchers have turned to parallel computing, which distributes the work across a cluster of machines. According to Professor Beng Chin Ooi, leader of the research group that developed SINGA:

It is essential to scale deep learning via distributed computing as…deep learning models are typically large and trained over big datasets, which may take hundreds of days using a single GPU.

There are two broad parallelism strategies for distributed deep-learning: data parallelism, where multiple machines work on different subsets of the input data, and model parallelism, where multiple machines train different sections of the neural-network model. SINGA supports both of these strategies, as well as a combination of the two. These strategies do introduce some communication and synchronization overhead, required to coordinate the work among the machines in the cluster. SINGA implements several optimizations to minimize this overhead.

Acceptance as a top-level project means that SINGA has passed several milestones related to software quality and community, which in theory makes the software more attractive as a solution. However, one possible barrier to adoption is that instead of building upon an existing API for modeling neural networks, such as Keras, SINGA’s designers chose to implement their own. By contrast, the Horovod framework open-sourced by Uber allows developers to port existing models written for the two most popular deep-learning frameworks, TensorFlow and PyTorch. PyTorch in particular is the framework used in a majority of recent research papers.

ASF has several other top-level distributed-data processing projects that support machine-learning, including Spark and Ignite. Unlike these, SINGA is designed specifically for deep-learning’s large models. ASF is also home to MXNet, a deep-learning framework similar to TensorFlow and PyTorch, which is still in incubator status. AWS touted MXNet as its framework of choice in late 2016, but MXNet still hasn’t achieved widespread popularity, hovering at just under 2% in KDNugget’s polls.

Apache SINGA version 2.0 was released in April, 2019. The source code is available on GitHub, and a list of open issues can be tracked in SINGA’s Jira project. According to ASF, upcoming features include “SINGA-lite for deep learning on edge devices with 5G, and SINGA-easy for making AI usable by domain experts (without deep AI background).

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Google has opened up the source code of two machine learning (ML) on-device systems, MobileNetV3 and MobileNetEdgeTPU, to the open source community.

In a blog post, software and silicon engineers Andrew Howard and Suyog Gupta from Google Research said on Wednesday that both the source code and checkpoints for MobileNetV3, as well as the Pixel 4 Edge TPU-optimized counterpart MobileNetEdgeTPU, are now available. 

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On-device ML applications for responsive intelligence have been designed with power-limited devices in mind, including our smartphones, tablets, and Internet of Things (IoT) electronics. 

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Google says the demand for mobile intelligence has prompted research into algorithmically-efficient neural network models and hardware “capable of performing billions of math operations per second while consuming only a few milliwatts of power,” such as in the case of the Google Pixel 4’s Pixel Neural Core. 

The latest MobileNet offerings include improvements to architectural design, speed, and accuracy, Google says. On mobile CPUs, users can expect MobileNetV3 to run at double the speed of MobileNetV2, bolstered through AutoML and NetAdapt, the latter of which has sliced away under-utilized activation channels. 

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A new activation function called hard-swish (h-swish) has also been implemented to improve functionality on mobile devices and reduce the risk of bottlenecks. Overall latency has been decreased by 15 percent and object detection latency has been reduced by 25 percent in comparison to MobileNetV2.

The MobileNetEdgeTPU model — similar to the Edge TPU in Coral products but tweaked for the camera features in Pixel 4 — now also has increased accuracy in comparison to earlier versions, while reducing both runtime and power requirements. 

Google did not set out to reduce the power demands of this model, but when compared to the basic MobileNetV3, MobileNetEdgeTPU consumes 50 percent less juice.

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MobileNetV3 and MobileNetEdgeTPU code can now be accessed from the MobileNet GitHub repository. 

Developers can also pick up a copy of open source implementation for MobileNetV3 and MobileNetEdgeTPU object detection from the Tensorflow Object Detection API page, and DeepLab is hosting the open source implementation for MobileNetV3 semantic

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Machine learning allows the algorithms that software and systems use to evolve as they process new data. Contrary to what many people think, humans play an integral role in this process and in applying machine learning-driven solutions to real-world problems.

Machine learning allows systems and AI engines to automatically learn and improve from experience. It is used to improve existing data processing systems or to create new software solutions.

Humans are closely involved in every step of producing technology that relies on machine learning. media updateunpacks the end-to-end role that humans play in machine learning, and how humans and AI complement one another.

Here’s a look at the numerous departments within a company that contribute to developing solutions powered by machine learning:

Sales and customer care teams

AI is currently a buzzword, with many companies experimenting with the capabilities of this ever-evolving technology. But, AI-powered solutions will only survive in the marketplace if they are purpose-built for a customer need, and that is why sales and customer care teams have to be involved in the development of the product.

“AI-powered solutions will only survive in the marketplace if they are purpose-built for a customer need.”

Salespeople and customer support teams deal with customers and clients, which means they are ideally positioned to identify specific consumer needs. This information is key to developing viable products, services, or solutions. This is especially true for developers of machine learning-driven solutions.

The role of these teams does not end at this point. Customers provide feedback on the products or solutions, and these insights can be used to improve the company’s offerings.

Developers, experts, and knowledge engineers

Once the clients’ needs have been established, IT development teams create the algorithms that form the basis of the machine learning system.

They gather relevant data from a number of sources inside and outside their company. Information and knowledge about the data are acquired from a subject matter expert, who is a specialist in the field related to the solution. Knowledge engineers enter this information into a knowledge base.

“Information and knowledge about the data are acquired from a subject matter expert, who is a specialist in the field related to the solution.”

An engine then creates models based on the data in the knowledge base. The development team evaluates the model that the engine has created and the results of new data it processes. They make necessary adjustments to the engine so that it processes the data correctly.

With this done, the technology is ready to be applied. It could be used in new software aimed at customers, such as automatic sentiment analysis of social media posts for media intelligence. Companies might apply the engine to their own internal systems to improve their data processing capabilities.

Marketers and creatives

Most solutions that make use of machine learning need a customer interface. User experience designers and graphic artists contribute to creating the navigation and design of this user-friendly platform.

With the machine learning solution in place, and working correctly, marketers brand the technology and find ways to make it attractive to customers. Machine learning-based offerings that are customer facing can fail without an effective marketing team who fully understand the capabilities of the solution.

Clients

Clients and customers are key to any business, and this is no different in the development of AI technology. Once clients are using the machine learning software, their feedback is vital to the development of the technology. Any shortcomings or errors they experience can help the developers improve the solution.

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