Putting an LP Spin on Video Analytics

Which analytics can give you a quick heads-up on potential loss activity?

• By Jackie Andersen
• Jun 01, 2014

Video analytics aren’t just tools for marketing and merchandising; these intelligent algorithms can also spot atypical customer behavior that might suggest potential loss activity when viewed by the Loss Prevention department.

By looking at four of the common video analytics used by retailers today, we can quickly see how departments can gain different business intelligence from the same applications.

Heat Mapping: Seeing Where Shoppers Go

Heat mapping lets retailers visualize customer traffic patterns through the store over time. This analytic uses color overlays to denote hot spots of activity and cold spots with little or no foot traffic.

Marketing and merchandising departments have been using this information for years to optimize store performance. They can see whether changing store layouts or moving a new promotional display to a hotter location improves merchandise performance. They can see where additional customer associates are needed to assist customers on a busy sales floor.

The LP spin. When LP reviews a heat map, those cold and hot spots take on a different meaning. Cold spots might indicate an area of the store where thieves could more easily conceal items. Hot spots might indicate a potential flash mob, especially if it suddenly comes out of nowhere with no underlying reasons for its appearance, like a doorbuster promotion. Knowing that crimes of opportunity often spike when there’s a lack of guardianship, an LP response to intelligence gained from heat mapping would be to step up staffing in hot and cold areas.

From an LP perspective, heat mapping is especially useful in static environments where the floor layout remains fairly constant. You can establish a good baseline of activity that makes it easier to judge spikes and anomalies.

Take, for instance, a cosmetics counter. You might have make-up artists in the aisles and sales associates behind the counter ringing up sales. But, if a popular cosmetics brand is offering a free gift with purchase that day, you might get a sudden influx of activity in the department. While the analytic might trigger an alert to management to make sure that there is sufficient staff in the area to ring up sales, LP will read the alert as a warning to beef up security and staffing to deter customers from walking off with testers and products.

Choosing the right camera. When it comes to heat mapping analytics, you don’t need a pricey, megapixel camera, as you’re only concerned about movement, not image details. A cost-effective fixed or dome camera with a good lens, a decent depth of field and either SVGA or 720p resolution would be sufficient for this application. Though the typical orientation is horizontal, you might want to operate the camera in corridor view for heat mapping long aisles.

Caveats. Because heat mapping relies on establishing a good baseline, it would be a challenge to deploy this analytic in locations where the floor plan configuration is constantly shifting. Without a reliable baseline, any comparative results would be erroneous. Also, if the location experiences a wide range of lighting conditions throughout the day—such as areas facing plate glass windows and doors— you will need a camera that supports wide dynamic range to compensate for glare.

Dwell Time: Discovering Where Shoppers Linger

Dwell time lets retailers see which areas of the store catch a customer’s eye and keep their attention. This analytic is designed to detect a person remaining stationary in a particular location beyond a set period of time, a parameter designated by the retailer. This application uses a graphic overlay on the video to pinpoint the location where the dwell threshold has been exceeded.

From a marketing perspective, dwell time analytics can indicate if a display is effective in attracting shopper interest. It can alert management to send a customer service associate to the location to convert the browser to a buyer and perhaps even upsell additional merchandise.

The LP spin. For LP, a dwell time alert can be a red flag that someone might be looking for an opportunity to conceal a product or scoping out the location for an organized retail theft sweep. In response, LP may simply send a store associate to inquire if the person needs assistance. Once the individual realizes they’re being observed, the window of opportunity for the concealment slams shut.

Consider a drugstore where shelves of baby formula might present a tempting target for organized retail crime gangs. If dwell time analysis indicates someone or several individuals are spending an inordinate amount of time comparing brands, LP might view the alert as a warning for a potential sweep and take proactive steps to avert it.

Choosing the right camera. When it comes to dwell time analytics, most retailers use fixed-dome cameras mainly because they provide the right depth-of-field and angle for good situational awareness. Plus, their unobtrusive design blends in well with a retail environment. If the camera is being used solely for dwell time analysis, SVGA or 720p resolution would be sufficient. But, if you’re using the camera for multiple purposes, you might want to choose a 1080p camera to capture facial details.

Caveat. LP will face additional challenges as organized retail crime becomes more sophisticated and incidents happen even faster. As with heat mapping, if the location experiences a wide range of lighting conditions throughout the day, you would need a camera that supports wide dynamic range to compensate for glare.

People Counting: Tallying Customer Traffic

People counting lets retailers keep track of how many shoppers are entering the store. This algorithm is designed traditionally to be one-directional so that it doesn’t count people leaving the premises. The information is generally presented in a graph broken down into units of time.

Retailers most often use people counting to calculate conversion rates and predict peak times when more staff is needed as traffic ebbs and flows. By aggregating this information over time, management can compare their store sales with other store’s—the Holy Grail of retailing.

The LP spin. When LP studies people-counting statistics, they’re looking to find a correlation between customer density and returns. By familiarizing themselves with the baseline—the average density of shoppers from sales to returns—they can spot anomalies that might indicate fraudulent rather than honest returns.

For instance, if LP discovers that the highest rate of return is occurring one or two hours before closing, they would likely conclude that these transactions bear further scrutiny. It could be that dishonest patrons are looking for times when staff is busy restocking shelves and closing down the store, making them too distracted to review return receipts carefully. Or, it could be that with few customers at the end of the day, an employee might be doing their own fraudulent returns. Upon further investigation, LP could discover tell-tale signs, like the absence of a customer and an associated credit card receipt linked to the original purchase.

In some retail establishments that operate stores within stores, management might install secondary people-counting cameras just to measure traffic to luxury specialty or high ticket item areas. For marketing and merchandising, it would be a way to calculate conversion rates and profitability of those locations separate from the overall performance of the store. But for LP, seeing unusual spikes in head count in those areas with highervalue merchandise might set off warning bells. This would be especially true if the secondary people-counting cameras were strategically deployed in a distribution center where high-end inventory, like expensive jewelry and accessories, were stored.

Choosing the right camera. A fixed dome would mostly likely be your go-to camera for people counting. Depending on the size of the area being covered and the type of detail you need to capture, you’re probably fine with a standard SVGA image. You could, however, bump all the way up to a five megapixel camera if you’re using the video for other situational awareness.

Caveat. It’s important to establish a baseline for typical customer traffic at different times of the day so that you can set alerts for deviations. You need to recognize that many peoplecounting analytics are self-learned and become more accurate over time. With continuous usage, they become smarter at discerning between human and non-human images, such as guide dogs, shopping carts and strollers, so occasionally recalibrate your baseline to ensure it reflects actual traffic into the store.

Facial Cataloging: Identifying Types of Shoppers

Facial cataloging is one of the new analytics gaining attention and traction in retail. This algorithm is designed to deduce the gender and approximate age of shoppers based on facial geometry. It can also predict general mood by analyzing facial expressions.

Marketing and merchandising departments often use facial cataloging in conjunction with smart monitors scattered throughout the floor. Once the video analytic determines the demographic of the individual in the vicinity of the monitor, the screen begins to stream content that might appeal to that gender and age group.

For instance, if facial cataloging identifies the shopper as a female between the ages of 40 and 50, the screen might display advertisements for wrinkle cream or accessories to complement the newest ready-to-wear apparel. If the shopper was recognized as a male, the promotional content might be for grooming tools or the latest men’s sportswear. The goal is to upsell the customer on additional purchases wherever the opportunity presents itself.

Facial cataloging can be used to alert management of the presence of VIP customers in the store so that personal shoppers can be dispatched to assist these customers with their purchases.

The LP spin. Facial cataloging helps LP quickly spot repetitive criminal behaviors by re-offenders or known retail crime members as they enter the premises by sending an alert to staff, who can then respond according to store policy and local laws. The reference facial database might include links to local law enforcement or other retailers’ watch lists.

In addition to identifying known suspects, LP can use facial cataloging to thwart internal theft. For example, if an associate repeatedly shows up in a high-ticket area of the store that is not part of their usual assigned territory, especially with no customer in tow, then that sends up a red flag for LP to further investigate the situation.

Choosing the right camera. When it comes to facial cataloging, you need a megapixel camera to capture enough facial details for identification. The camera has to be mounted with no more than a 9 degree slope or you’ll end up with hair follicle recognition instead. Depending on the lighting levels, you may need a camera with wide dynamic range or lowlight technology to deliver usable images, especially in dynamicallychanging lighting conditions. Another thing to keep in mind is that cameras used for facial cataloging should only be used for that single purpose.

Caveat. The biggest roadblock to implementing facial cataloging is privacy rights, which are regulated on a state-by-state basis.

In the beginning, video analytics were primarily used for marketing and merchandising, but as these algorithms integrate into standard operating procedures, their cross-functional value is becoming even more apparent. When LP applies this intelligence to its surveillance toolbox, the ability to anticipate and avert loss increases significantly, as does the retailer’s overall return on investment.

This article originally appeared in the June 2014 issue of Security Today.