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Machine Studying (ML), AI, Laptop Imaginative and prescient (CV)… you’d be exhausting pressed to search out somebody who hasn’t a minimum of heard of those matters. They dominate fashionable tradition and are on the forefront of a brand new group of technological potentialities. A wonderful instance is the Meraki MV digital camera – a digital camera that stretches the definition of ‘digital camera’. Powered by ML and CV, the MV is a visible sensor, providing crucial insights about individuals and automobiles in your bodily areas (be taught extra right here!). These insights deal with the counting & movement of objects in body at a selected time, and they’re relevant to quite a lot of use circumstances.
Whereas many challenges will be addressed with the MV’s native capabilities, what if our use case is extra advanced? We is likely to be concerned about detecting topics aside from individuals or automobiles. Maybe we have to classify a car by make & mannequin, or classify an individual by age. How will we deal with these use circumstances?
One solution to remedy these challenges is to pair the MV with a customized ML mannequin. We will leverage the MV’s snapshots, carry out picture evaluation utilizing our ML mannequin, and act on the outcomes. Another choice is the brand new “Customized Laptop Imaginative and prescient” characteristic. This characteristic permits us to insert our personal ML mannequin straight onto the digital camera, subscribe to a MQTT subject, and extract classification information. What would these concepts seem like in follow? Nicely let’s take a look at actual examples of the place our workforce applied these concepts to assist Cisco prospects remedy distinctive challenges. Every case is exclusive, however all of them share widespread parts: an MV Digicam, a customized ML Mannequin, and a little bit little bit of customized code.
Individuals Detection 2.0
On this instance, we labored with a retail buyer concerned about creating a personalised buying expertise utilizing digital signage and Meraki MV’s. Our workforce used a mix of MV Individuals Detection, Python, and AWS’s Rekognition Engine to boost the shopper buying expertise. The answer detects buyers, identifies their age & intercourse, and makes use of digital signage to show information pushed, related product strains. The answer not solely improved the shopper’s expertise however result in elevated gross sales and additional analytics round buyer foot site visitors and demographics. Try the code right here.
Automobile Detection 2.0
On this instance, we labored with a buyer concerned about enhancing their curbside pickup expertise by decreasing buyer wait instances. Our workforce constructed an answer that detected a automobile, matched the license plate to an order, and despatched a Webex notification informing staff a buyer with the corresponding order was ready. Powered by Flask, MV Automobile Detection, and the Google Imaginative and prescient Engine for Plate Detection, the answer considerably decreased curbside pickup time and elevated worker effectivity. Try the code right here.
Past Individuals and Vehicles
In our ultimate instance, a buyer wanted to simply implement masks utilization in a big public venue throughout Covid-19. Our answer leveraged the MV’s RTSP Video Feed, Python, and a customized TensorFlow Masks Mannequin to research MV footage for masks utilization. If an individual in body was not sporting a masks, the code sends a Webex alert and snapshot to safety personnel. This answer helped the venue meet compliance and security restrictions through the pandemic. Try the code right here.
Cameras & ML Fashions vs. The World
The Meraki MV Cameras provide a robust platform for automation, however typically a use case goes past native capabilities. Thankfully, with a little bit of customized code & a customized mannequin, it’s simpler than ever to increase an MV and deal with a buyer’s most tough challenges.
In the event you’re concerned about studying extra concerning the examples, take a look at the hyperlinks beneath. Every repository incorporates the pattern code & directions for the best way to use it in your personal community:
About our GVE workforce
The International Digital Engineering (GVE) DevNet workforce works with Cisco prospects to assist deliver their automation concepts to life. Along with Cisco Account Groups, we discover alternatives the place prospects want a little bit assist getting began with automation or integration initiatives. We develop easy examples to showcase what is feasible with a little bit little bit of customized code. Many of those instance initiatives are revealed on the GVE DevNet GitHub web page and shared with the group.
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