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EETE SEP 2014

optoelectronics VTT depth cameras interpret shoppers’ behaviour By Julien Happich In paper titled “Shopper Behaviour Analysis Based on 3D Situation Awareness Information” presented during the 22nd International Conference on Pattern Recognition, researchers from the VTT Technical Research Centre of Finland detail an advanced tracking system based on depth cameras. The research focuses mainly on behaviour pattern analysis based on data fusion across a network of low-cost ceiling-mounted depth cameras (measuring the distance to a variety of surfaces with the help of a laser dot pattern operating in the IR range). With appropriate refresh rates and data fusion performed across a single coordinate system for unified tracking spanning the physical space covered by the cameras, the video analytics algorithms are able to track moving customers in a wide range of lighting conditions (including complete darkness), and can handle view occlusions that are typically difficult to interpret using 2D cameras. In a histogram fashion, traffic heat maps can be superimposed over the shop’s floor to analyse how the products layout impacts the consumers’ whereabouts and dwelling time. In the paper which will be part of a workshop on “Video Analytics for Audience Measurement in Retail and Digital Signage”, the researchers explain that depth sensors are less privacy-threatening than 2D or 3D stereo cameras, as they do not provide actual photographic information. Though that may still be combined with other aspects of audience measurement as they are considered in this workshop, such as gender recognition, age group estimation, ethnicity recognition, emotion analysis, or free eye gaze estimation (think about adaptive displays that trigger their advertising message when you gaze in their direction). Anyhow, as a way to keep up with online competition, the retail and advertisement industries are seeking more data out of their customers’ in-shop behaviour to measure their engagement with products, booths, or newly launched campaigns. And store performance optimization, as the paper describes it, comes in the shape of always-on analytics. Exact real-time information on customer behaviour goes beyond today’s point-of-sale data analysis and opens up new adaptive advertising scenarios to funnel more customers towards the cashier. Doing so, the researchers identified three classes of motion patterns to be acted upon: passers-by in passage areas, decisive customers also dubbed “quick shoppers” as these know precisely what they are looking for, and exploratory customers also qualified as “slow shoppers”. From a retailer’s perspective, identifying these classes of motion is an enabler, since different classes of advertisements (concise or more detailed) will be served to different types of customers. Sound and lighting effects distributed throughout the shop could even come into play for the most elaborate scenarios. In fact, retail shops of the future may well have to display warning signs that by entering their premises, you agree to be subconsciously manipulated to the maximum extent permitted by law. Depth versus 2D cameras “On the sensing side, with off-the-shelf depth sensors, the detection range is about 8 meters with a accuracy of 1 to 10cm depending on the distance from the sensor. With upcoming ste- In a histogram fashion, traffic heat maps can be superimposed over the shop’s floor to analyse how the products layout impacts the consumers’ whereabouts and dwelling time. 40 Electronic Engineering Times Europe September 2014 www.electronics-eetimes.com


EETE SEP 2014
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