Four Ways IoT, AI and Computer Vision Can Make Your Life Better
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Imagine leaving your office or residence and knowing you won't have to wait more than four minutes to pick up the groceries you already ordered ahead online. What's more, you won't be stuck in traffic congestion on the way home. Perhaps you will be living in a city which actively manages to a goal of zero traffic incidents overall.
Who wouldn't love these quality of life conveniences? Well, all this could be possible thanks to IoT and AI-powered video analytics.
It is expected that over 1 billion cameras will be deployed in the world by 2021, according to a 2019 study issued by IHS Markit and reported in CNBC and the Wall Street Journal. Today, cameras are already the largest data generator on the planet. With computer vision, it is now possible to understand videos automatically, turning that large amount of rich visual content into real-time analytics and valuable insights.
What can we expect from AI and computer vision in our day-to-day life moving forward? Here are four examples of how this new technology can be used in a positive way to make your life better.
Imagine parking in a retail store pick-up bay and the store already knows you've just arrived by your vehicle and/or parking spot number. As a result, your order has automatically been initiated and is on its way to you in a seamless fashion, greatly reducing wait times and giving back time to finish a call with work, family or friends.
Innovative retail outlets are rapidly moving in this direction and implementing this into the new retail experience.
With IoT and AI, cities and local governments monitor and quantify traffic flow, assess changing patterns and detect and report anomalies and traffic incidents in real-time. It also provides cities and communities with valuable insights for the implementation and management of appropriate resources (think road repair, debris on the road, snow removal, abandoned car or wildlife), as well as lifesaving methodologies and policy enforcement recommendations — if needed — to ensure public safety and welfare.
AI-powered computer vision traffic systems passively gather traffic data to measure the types and volumes of various vehicles traveling on the roads. These systems provide planners with insights on how to better understand traffic flows and congestion areas, as well as predict the wear and tear of streets, roads and highways. This information can then be used to plan for repairs or consider upgrades to these roadways to avoid disruption and ensure commerce and private travels are pleasant and unhindered.
Analysis of accident trends helps to identify the precursors to accidents and allows planners to try and mitigate those issues to prevent future accidents. It is also possible to act upon real-time information.
For example, imagine a tired driver making a wrong turn and unknowingly driving the wrong way down a road or highway. The city system detects this problem early and alerts local authorities immediately to try and intervene and prevent an accident or any potential loss of life as a result. Another example of a common situation would be a stalled car on the side of the highway. The system would automatically trigger a real-time alert where local authorities can send immediate help and roadside assistance.
Picture a group of people returning from lunch, walking down the street and approaching the crosswalk. As they prepare to cross, an audio announcement warns them that there are cars approaching from around a corner or the other side of a hill, and to wait until they pass. This announcement was created by a video sensor detecting traffic approaching and signaling the audio announcement to play; thereby, warning the preoccupied, unaware pedestrians to wait until the oncoming cars pass and it is safe to cross the street.
Or imagine children walking to school every day in the morning and returning home in the afternoon. They are able to cross the street safely and easily because IoT and AI-powered analytics have informed the city DOT that the lights should be extended (using signal phasing and timing) to allow large groups to cross during these times but return to normal timing after a certain time period or summertime hours.
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