|This article needs additional citations for verification. (April 2015)|
A people counter is a device used to measure the number and direction of people traversing a certain passage or entrance, often used at buildings, so that the total number of visitors can be recorded.
- 1 Use cases
- 2 Business Metrics
- 3 Technologies
- 4 References
Conversion Rate - The use of people counting systems in the retail environment is necessary to calculate the conversion rate, i.e., the percentage of a store's visitors that makes purchases. This is the key performance indicator of a store's performance and is superior to traditional methods, which only take into account sales data. Together, traffic counts and conversion rates how a store arrived at sales, e.g., if year-over-year sales are down, did fewer people visit the store, or did fewer people buy?
Marketing Effectiveness - Shopping mall marketing professionals rely on visitor statistics to measure their marketing. Often, shopping mall owners measure marketing effectiveness with sales, and also use visitor statistics to scientifically measure marketing effectiveness. Marketing metrics such as CPM (Cost Per Thousand) and SSF (Shoppers per Square Foot) are performance indicators that shopping mall owners monitor to determine rent according to the total number of visitors to the mall or according to the number of visitors to each individual store in the mall.
Staff Planning - Accurate visitor counting is also useful in the process of optimizing staff shifts; Staff requirements are often directly related to density of visitor traffic and services such as cleaning and maintenance are typically done when traffic is at its lowest.
Museums and libraries
Many non-profit organizations use visitor counts as evidence when making applications for finance. In cases where tickets are not sold, such as in museums and libraries, counting is either automated, or staff keep a log of how many clients use different services.
The number of people coming in and out of the venue.
Window conversion rate
Window Conversion Rate is the percentage of shoppers who came into the store over the people of people who walk passed the outside of the store. With WiFi counting shops can estimate the number of people walked passed the store. A more accurate method is video counting. While revenue and footfall are important, the number of people who walked pass the store often reflects the true potential of the store location. The Window Conversion Rate often depends on the attractiveness of the shop window design and the effectiveness of marketing campaigns.
Visit duration is the amount of time visitors stay in the venue. WiFi counting has the ability to track the time when a person carrying a smartphone has entered the venue and when that same person has left the venue.
The number of people came into the store who had visited the store previously. WiFi counting has the ability to remember the Unique WiFi beacon signal id emitted by shoppers, so if a shopper had previously visit the store, the counter would flag the person as a returning customer.
The number of shoppers who came into the store had previously visited other stores of the same chain. This is available for 3rd generation people counters that have WiFi counting functionalities.
1st generation - Infrared beam counters
The simplest form of counter is a single, horizontal infrared beam across an entrance which is typically linked to a small LCD display unit at the side of the doorway. Such a beam counts a 'tick' when the beam is broken, therefore it is normal to divide the 'ticks' by two to get visitor numbers. Beam Counters usually require a receiver or a reflector mounted opposite the unit with a typical range from 2.5 metres (8 ft 2 in) to 6 metres (20 ft). Despite its limitations, infrared counters are still widely used, primarily due to its low cost and simplicity of installation.
2nd generation: Thermal counters
Thermal imaging systems use array sensors which detect heat sources. These systems are typically implemented using embedded technology and are mounted overhead for high accuracy.
Before the advance of computer technology which allows complex algorithms to perform video counting, thermal counters were the main choice for most businesses. It can deliver reasonably high accuracy; however it does have its limitations, such as
i) thermal counters cannot mount on a high ceiling
ii) can only cover a narrow door entrance
iii) difficult to verify the accuracy of the counter
iv) accuracy reduces in places there is slight variations in thermal conditions.
3rd generation: Video + WiFi counting
With the advance of computer technology, complex image processing algorithm can now be used to perform counting using camera imaging. The third generation counters also includes Wifi Counting functionality which collects WiFi probe request signals from shoppers' smartphones and add a number of important metrics for businesses, especially for the retail industry. Many business are now deploying people counters to help them to gain insights into their businesses 
Computer vision carries out its processes inside an embedded device. This reduces network bandwidth requirements as only the counting data has to be sent over the network. Robust and adaptive algorithms has been developed to provide excellent counting accuracy for both outdoor and indoor counting using computer vision. Multilayer Background Subtraction, based on colour and texture, is considered the most robust algorithm available for varying shadows and lighting conditions. With the advances in image processing, video count can achieve 90-95% in various lighting environments. The use of artificial intelligence and pattern recognition functions can further enhance its accuracy.
WiFi Counting uses WiFi receiver to pick up unique WiFi management frames emitted from the smartphones with a range of up to 100 metres. While not all people carry a smartphone, WiFi counting can produce statistically significant metrics due to the large sample size available. Apple iOS8 attempts to randomise MAC address, however it is making little impact on the effectiveness of WiFi counting.
- Dillon, Chris (30 July 2015). "Sunder Sandher’s tech game changer".
- Paul C. Box, Joseph C. Oppenlander (1976), Manual of traffic engineering studies, Institute of Transportation Engineers, p. 17, retrieved December 21, 2010
- Technologies, Zebra (2015). "Analysis of iOS 8 MAC Randomization on Loca tioning" (PDF).