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Counting People with Andrew Farah

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Von Hackers – Software Engineering Daily entdeckt von Player FM und unserer Community - Das Urheberrecht hat der Herausgeber, nicht Player FM, und die Audiodaten werden direkt von ihren Servern gestreamt. Tippe auf Abonnieren um Updates in Player FM zu verfolgen oder füge die URL in andere Podcast Apps ein.

If you operate a restaurant, you want to know how many people are inside your restaurant at any given time. You also want to be able to know your occupancy if you operate a movie theater, coffee shop, or apparel store.

Knowing how many people are in your building can answer several business-related questions. Do you need to unlock an additional entrance? Should you open another store? Do you really need a building this big?

This might sound like a simple question, but how do you solve the problem of counting people inside of a building?

A naive approach to counting people is to use video cameras and count the number of people entering and exiting the building. Machine learning algorithms are good at classifying humans. But the downside of this is that you have to put cameras anywhere you want a people-counter. There are many situations where you would want to count the number of people where a camera is not acceptable. What if you wanted to count people in a privacy preserving way? What if you wanted to obscure any identifiable traits of a person that you were counting?

Density is a device for counting people. It sits above a doorway and counts the people who are entering or exiting the building. Andrew Farah is the CEO at Density, and in today’s episode he explains why the problem of counting people is harder than it sounds, and how the Density people counter functions.

The post Counting People with Andrew Farah appeared first on Software Engineering Daily.

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103 Episoden

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Fetch error

Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on December 23, 2022 09:47 (6M ago)

What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.

Manage episode 212818392 series 1436861
Von Hackers – Software Engineering Daily entdeckt von Player FM und unserer Community - Das Urheberrecht hat der Herausgeber, nicht Player FM, und die Audiodaten werden direkt von ihren Servern gestreamt. Tippe auf Abonnieren um Updates in Player FM zu verfolgen oder füge die URL in andere Podcast Apps ein.

If you operate a restaurant, you want to know how many people are inside your restaurant at any given time. You also want to be able to know your occupancy if you operate a movie theater, coffee shop, or apparel store.

Knowing how many people are in your building can answer several business-related questions. Do you need to unlock an additional entrance? Should you open another store? Do you really need a building this big?

This might sound like a simple question, but how do you solve the problem of counting people inside of a building?

A naive approach to counting people is to use video cameras and count the number of people entering and exiting the building. Machine learning algorithms are good at classifying humans. But the downside of this is that you have to put cameras anywhere you want a people-counter. There are many situations where you would want to count the number of people where a camera is not acceptable. What if you wanted to count people in a privacy preserving way? What if you wanted to obscure any identifiable traits of a person that you were counting?

Density is a device for counting people. It sits above a doorway and counts the people who are entering or exiting the building. Andrew Farah is the CEO at Density, and in today’s episode he explains why the problem of counting people is harder than it sounds, and how the Density people counter functions.

The post Counting People with Andrew Farah appeared first on Software Engineering Daily.

  continue reading

103 Episoden

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