Augmented Reality with Machine Learning- Applications and Moving Beyond the Tracking Marker

Machine learning is coming into a Golden Age, and with it we’re seeing an awakening of possibilities formerly reserved for science fiction.

Machine Learning (ML) is a computers way of learning from example, and it’s one of the most useful tools we have for the construction of Artificial Intelligence (AI). It begins with the design of an algorithm that learns from collected data, creating machines that in most cases become smarter as data volumes intensify. We’ve seen a breakthrough in the field of ML in the last five years in part due to the recent wealth of big data streams provided from high-speed internet, cloud computing, and widespread smartphone usage, leading to the birth of the now “Deep Learning” algorithms.

Heavily used applications that have emerged with ML at their core include face recognition technology seen in Facebook, email spam filters from Google and Microsoft and speech recognition systems such as Siri. While the depth of advancement is unknown, what we can say with high certainty is that development in this field in the past five years will be nothing compared to what we’re going to see in the five years to come.

Based on machine learning’s current state we can predict something:

Image-Based Recognition

Image based technology is on the horizon and with a whole lot user experience. Thanks to deep learning we are at dawn of computers recognising images and the people and actions within them with high accuracy based on the image alone. It’s not just new pictures that will become recognizable either but the entire history of digitized images and video footage. For example YouTube soon might intelligently find content related to parts of a clip you watched and liked based on the visual content of the video itself.

Driverless car

The first fleet of self-driving cars may be ready by 2020. Google has said that its car should be ready by 2020 and Tesla CEO, Elon Musk, says their Tesla’s will be fully autonomous by 2018. It appears that these driverless cars are still in the data gathering stage. Since increased safety is one of the top goals for these future cars, it is important for them to harvest great amounts of data in order for them to respond in the moment and react to potentially dangerous situations. This technology aims to increase driver safety for a more convenient driving experience. If augmented reality cars become more popular, they have great potential to make the transition to driverless cars smoother. Since we may have to wait up to 5-10 years before we see autonomous care take over the roads, the process of driving automation will have to be gradual.

Healthcare

Machine learning’s ability to analyse and store massive amounts of data should provide physicians with much-needed second opinions and lead to the detection and treatment of medical ailments on a mass scale. Packaged as smart, wearable computing devices, personal health monitors that detect various conditions as they arise should become widespread in the next five years, in a similar fashion to activity trackers like Fitbit. The advancements could significantly accelerate our human desire to protect our own longevity and create major breakthroughs for the operations of the medical industry.

Travel and communication

Augmented Reality for mobile devices has grown in popularity in recent years partly because of the proliferation of smart phones and tablet computers equipped with exceptional cameras and partly because of developments in computer vision algorithms that make implementing such technologies on embedded systems possible.
Such augmented reality applications have always been limited to a single user receiving additional information about a physical entity or interacting with a virtual agent. Researchers at MIT’s Media Lab have taken augmented reality to the next level by developing a multi-user collaboration tool that allows users to augment reality and share that we other users essentially turning the real world into a digital canvas for all to share.

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Ritesh
administrator
Ritesh Kanjee has over 7 years in Printed Circuit Board (PCB) design as well in image processing and embedded control. He completed his Masters Degree in Electronic engineering and published a paper for IEEE called Vision-based adaptive Cruise control using Pattern matching (on Google Scholar). His work was implemented in LabVIEW. He works as an Embedded Electronic Engineer in defence research. He has experience in FPGA design with programming in both VHDL and Verilog.