Point of sale integration with video analytics for fraud detection

A method of operating a video system to detect fraudulent return transactions comprises receiving an indication of a return transaction from a point of sale system, wherein the indication of the return transaction indicates a time when the return transaction occurred. The method further comprises receiving video data of an area proximate to the point of sale system captured at the time when the return transaction occurred. The method further comprises processing the video data to determine whether a first person is located in a return area and whether a second person is located proximate to the point of sale system. The method further comprises determining whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system.

TECHNICAL FIELD

Aspects of the invention are related, in general, to the field of video processing and, more specifically, to fraud detection of return transactions at a point of sale system and determining item conversion rates.

TECHNICAL BACKGROUND

Commercial enterprises, such as retail establishments, attempt to carefully monitor store inventory and the details of sales and return transactions with customers. Typically, a sales clerk operates a point of sale system or cash register to conduct sales and return transactions. When monitoring customer transactions, store owners are often concerned with detecting fraud and, in particular, fraudulent sales and return transactions. Unfortunately, the perpetrators of a fraudulent return transaction are often the employees entrusted to operate the point of sale system.

Another area of concern to an owner of a retail establishment is conversion rate statistics. A conversion rate is a metric that relates a number of customers who enter an area, such as a retail establishment, to a number of sales transactions that occur during a time period. Typically, a conversion rate is expressed as a percentage of sales relative to a number of shoppers. Retailers may utilize conversion rate statistics to determine which products are selling well, and which areas of the store are producing the most sales per shopper.

OVERVIEW

A method of operating a video system to detect fraudulent return transactions is disclosed. The method comprises receiving an indication of a return transaction from a point of sale system, wherein the indication of the return transaction indicates a time when the return transaction occurred. The method further comprises receiving video data of an area proximate to the point of sale system captured at the time when the return transaction occurred. The method further comprises processing the video data to determine whether a first person is located in a return area and whether a second person is located proximate to the point of sale system. The method further comprises determining whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system.

In an embodiment, a computer-readable medium has stored thereon program instructions for operating a video system to detect fraudulent return transactions, wherein the program instructions, when executed by a computer system, direct the computer system to receive an indication of a return transaction from a point of sale system, wherein the indication of the return transaction indicates a time when the return transaction occurred. The program instructions further direct the computer system to receive video data of an area proximate to the point of sale system captured at the time when the return transaction occurred. The program instructions further direct the video source system to process the video data to determine whether a first person is located in a return area and whether a second person is located proximate to the point of sale system. The program instructions further direct the video source system to determine whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system.

In an embodiment, determining whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system comprises determining that the return transaction is legitimate when the first person is located in the return area and the second person is located proximate to the point of sale system.

In an embodiment, determining whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system comprises determining that the return transaction is legitimate when the first person is located in the return area, no person is located proximate to the point of sale system, and the second person is located proximate to a different point of sale system.

In an embodiment, determining whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system comprises flagging the return transaction as potentially fraudulent when the first person is located in the return area, the second person is located proximate to the different point of sale system, and a third person is located proximate to the point of sale system.

In an embodiment, determining whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system comprises flagging the return transaction as potentially fraudulent when the first person is not located in the return area and the second person is located proximate to the point of sale system.

In an embodiment, the return transaction is provided for review if the return transaction is determined to be fraudulent.

In an embodiment, receiving the video data of the area proximate to the point of sale system captured at the time when the return transaction occurred comprises, in response to receiving the indication of the return transaction from the point of sale system, requesting the video data captured at the time when the return transaction occurred and responsively receiving the video data of the area proximate to the point of sale system captured at the time when the return transaction occurred.

In an embodiment, processing the video data to determine whether the first person is located in the return area and whether the second person is located proximate to the point of sale system comprises analyzing the video data to identify the return area and an area proximate to the point of sale system.

In an embodiment, analyzing the video data to identify the return area comprises analyzing the video data to recognize predetermined coordinates of the return area relative to the video data.

In an embodiment, a method of operating a video system to detect fraudulent return transactions is disclosed. The method comprises receiving an indication of a return transaction from a point of sale system, wherein the indication of the return transaction indicates a time when the return transaction occurred. The method further comprises receiving video data of an area proximate to the point of sale system captured at the time when the return transaction occurred. The method further comprises processing the video data to determine whether a first person is located in a return area and whether a second person is located proximate to the point of sale system. The method further comprises, if the first person is located in the return area and the second person is located proximate to the point of sale system, determining that the return transaction is legitimate. The method further comprises, if the first person is located in the return area, no person is located proximate to the point of sale system, and the second person is located proximate to a different point of sale system, determining that the return transaction is legitimate. The method further comprises, if the first person is located in the return area, the second person is located proximate to the different point of sale system, and a third person is located proximate to the point of sale system, flagging the return transaction as potentially fraudulent. The method further comprises, if the first person is not located in the return area and the second person is located proximate to the point of sale system, flagging the return transaction as potentially fraudulent.

DETAILED DESCRIPTION

The following description and associated drawings teach the best mode of the invention. For the purpose of teaching inventive principles, some conventional aspects of the best mode may be simplified or omitted. The following claims specify the scope of the invention. Some aspects of the best mode may not fall within the scope of the invention as specified by the claims. Thus, those skilled in the art will appreciate variations from the best mode that fall within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. As a result, the invention is not limited to the specific examples described below, but only by the claims and their equivalents.

FIG. 1provides a flow diagram of a method100according to an embodiment of the invention for operating a video system to detect fraudulent return transactions. In the method100, an indication of a return transaction from a point of sale system is received, wherein the indication of the return transaction indicates a time when the return transaction occurred (101). The point of sale system is typically in communication with the video system and provides the indication of the return transaction over a communication network or some other technique of communicating information. For example, the point of sale system could be configured to transmit the indication of the return transaction in response to a store clerk activating a return transaction routine on the point of sale system. In other examples, the point of sale system could be configured to provide the indication of the return transaction at a later time after the return transaction has been completed, such as during a time when network bandwidth is available or after store hours.

The method continues when video data of an area proximate to the point of sale system captured at the time when the return transaction occurred is received (102). The video data could be received prior to, concurrently, or after receiving the indication of the return transaction from the point of sale system. The physical area proximate to the point of sale system, which may be a store or other retail establishment, could include a number of items, such as a return area, checkout lanes, aisles, customers, store clerks, point of sale systems, items for sale, and other objects typically located within a retail establishment. In some examples, a video system may request the video data in response to receiving the indication of the return transaction. For example, the video system may request the video data of the area proximate to the point of sale system associated with the return transaction captured at the time when the return transaction occurred based on the time included in the indication of the return transaction.

The video data is then processed to determine whether a first person is located in a return area and whether a second person is located proximate to the point of sale system (103). Typically, the video system may process the video data to determine the location of the return area by identifying a colored boundary line marked on the floor of the area around the return area, ropes or metal bars designating a return lane, different coloration or markings on the floor of the return area, radio frequency (RF) tags such as an RFID emitted from the return area or the boundary lines of the return area, predetermined coordinates of the return area relative to the video data, or some other technique of identifying the return area. Similar techniques may also be used to identify the point of sale system and the area proximate thereto, including identification of the shape, size, and physical features of the point of sale system in use at the retail establishment. In addition, the first person and the second person can be determined by identifying a shape and size matching that of a human, movement by the person that can be attributed to human motion, facial recognition techniques, clothing worn by the person, such as a uniform, hat, or vest, identification tags, an RF tag emitting an RFID worn by store clerks or employees, or any other technique for analyzing video data to identify humans appearing in the video. Note that the video data could be analyzed in real-time as the video data is captured “live”, or could be stored in a storage system and analyzed subsequent to the captured action in post-processing.

If the first person is located in the return area and the second person is located proximate to the point of sale system, the system determines that the return transaction is legitimate (104). In a typical return transaction scenario, the first person located in the return area is a customer attempting to return an item, and the second person located proximate to the point of sale system is a store clerk operating the point of sale system to perform the return transaction on behalf of the customer. In this situation, the video system determines that the return transaction is likely legitimate.

If the first person is located in the return area, no person is located proximate to the point of sale system, and the second person is located proximate to a different point of sale system, the system determines that the return transaction is legitimate (105). In this scenario, the first person located in the return area is likely a customer requesting to return an item previously purchased, and the second person located at the different point of sale system is likely operating the different point of sale system to effectuate the return transaction on that different point of sale system instead of the point of sale system that initially provided the indication of the return transaction to the video system in operation101. Thus, the video system determines that the return transaction in this situation is also legitimate.

If the first person is located in the return area, the second person is located proximate to the different point of sale system, and a third person is located proximate to the point of sale system, the video system flags the return transaction as potentially fraudulent (106). In this case, although the first person who is located in the return area is likely a customer who may have requested to return an item, since there is a second person located proximate to a different point of sale system and a third person located proximate to the point of sale system, the second person located at the different point of sale system and/or the third person located at the point of sale system could possibly be conducting a fraudulent return transaction. Thus, the video system flags the return transaction as potentially fraudulent in this case.

Finally, if the first person is not located in the return area and the second person is located proximate to the point of sale system, the video system flags the return transaction as potentially fraudulent (107). In this scenario, since no first person is located in the return area, there is no customer attempting to return an item. The second person located proximate to the point of sale system is likely a sales clerk conducting a fraudulent transaction, and so the video system flags the return transaction as potentially fraudulent in this situation.

Advantageously, a video system operating according to the method ofFIG. 1will have a higher likelihood of detecting potentially fraudulent return transactions at a retail establishment, thereby allowing a store owner, manager, supervisor, or some other appropriate individual to review the details of the return transaction to verify any potential fraud that may have occurred. WhileFIG. 1may indicate a particular order of execution, other orders of execution, including concurrent or simultaneous execution, may be possible while remaining within the scope of the invention. In another implementation, the method100portrayed inFIG. 1may be embodied as a non-transitory computer readable hardware medium having instructions encoded thereon that are executable on a processing system for implementing the method100.

FIG. 2is a block diagram illustrating a system200for detecting fraudulent return transactions. The system200includes a communication interface202configured to receive video206of a return area221and one or more point of sale systems222and223within a physical area220. In some examples, communication interface202is configured to receive video data206of an area proximate to a point of sale system222and/or223at a time when a return transaction occurred. In addition, the communication interface202is configured to receive an indication of a return transaction from a point of sale system222and/or223over communication link225, wherein the indication of the return transaction indicates a time when the return transaction occurred.

In the example ofFIG. 2, the video206is generated by one or more video cameras210and transferred to the communication interface202. In one implementation, the video cameras210are included as part of the system200. Also residing in the system200is a processing system204configured to process the video data206to determine whether a first person is located in the return area221and whether a second person is located proximate to a point of sale system222. The processing system204is further configured to determine that the return transaction is legitimate if the first person is located in the return area221and the second person is located proximate to a point of sale system222. The processing system204is further configured to determine that the return transaction is legitimate if the first person is located in the return area221, no person is located proximate to the point of sale system222, and the second person is located proximate to a different point of sale system223. The processing system204is further configured to flag the return transaction as potentially fraudulent if the first person is located in the return area221, the second person is located proximate to the different point of sale system222, and a third person is located proximate to the point of sale system223. The processing system204is further configured to flag the return transaction as potentially fraudulent if the first person is not located in the return area221and the second person is located proximate to the point of sale system222.

FIG. 3is a block diagram illustrating a physical area300to be analyzed by the system200ofFIG. 2according to an embodiment of the invention. The physical area300includes a return area320, point of sale systems301-304, and persons310and325located therein. In one example, the physical area300may be the interior of a retail environment, such as a grocery store, department store, or flea market, including items for sale, for rent, or for another purpose. In another implementation, the physical area300may be an exhibit or convention hall containing exhibits, booths, displays, or other structures. An amusement park or carnival offering rides, games, and other attractions may be viewed as the physical area300in another example. The physical area300need not be an enclosed area defined by walls, fences, or similar structures, but may be any area that may be defined or delineated. Other examples of the physical area400and the items contained therein may also provide the environment in which the systems and methods disclosed herein operate.

Person325is located within return area320. Return area320can be designated in a number of ways so that system200is able to determine the area within physical area300that comprises return area320. For example, return area320could be designated by lines on the floor, colors, textures, tiles, signs, ropes, bars, or other physical distinctions within physical area300. In addition, system200could be configured to recognize return area320by predetermined coordinates, such as Global Positioning System (GPS) coordinates or x-y coordinates relative to the video data206, or by RFID data broadcast by an active RF tag embedded around or within return area320.

In the example ofFIG. 3, person310is standing behind point of sale system302, while person325is located within return area320. System200receives an indication of a return transaction initiated on point of sale system302. Based on the relative positions of persons310and325, system200determines that person310is likely a store clerk operating point of sale system302to effectuate a return transaction for person325. Since no other persons are located within physical area300, system200determines that the return transaction initiated on point of sale system302is legitimate, and thus does not flag the return transaction as potentially fraudulent.

FIG. 4is a block diagram illustrating a physical area400to be analyzed by the system200ofFIG. 2according to an embodiment of the invention. The physical area400includes a return area420, point of sale systems401-404, and persons410and425located therein. Person425is located within return area420. Return area420can be designated in a number of ways so that system200is able to determine the area within physical area400that comprises return area420.

In the example ofFIG. 4, person410is standing behind point of sale system401, while person425is located within return area420. System200receives an indication of a return transaction initiated on point of sale system402. Based on the relative positions of persons410and425, system200determines that person410is likely a store clerk operating point of sale system402to effectuate a return transaction for person425. For example, store clerk410may have initiated the return transaction on point of sale system402, but is now operating point of sale system401to retrieve additional cash for the return or to otherwise complete the return transaction using point of sale system401. Since no other persons are located proximate to the other point of sale systems402-404that could be assisting customer425with the return transaction, system200determines that the return transaction initiated on point of sale system402is legitimate, and thus does not flag the return transaction as potentially fraudulent.

FIG. 5is a block diagram illustrating a physical area500to be analyzed by the system200ofFIG. 2according to an embodiment of the invention. The physical area500includes a return area520, point of sale systems501-504, and persons510,515, and525located therein. Person525is located within return area520. Return area520can be designated in a number of ways so that system200is able to determine the area within physical area500that comprises return area520.

In the example ofFIG. 5, person510is standing behind point of sale system501, person515is standing behind point of sale system502, and person525is located within return area520. Based on the relative positions of persons510,515, and525, system200determines that persons510and515are likely store clerks operating respective point of sale systems501and502, while person525standing within return area520is likely a customer requesting to return an item. In this example, if system200receives an indication of a return transaction initiated on point of sale system501, system200would determine that the return transaction is likely fraudulent, since clerk510is not helping another customer and clerk515appears to be helping person525. Even if system200receives an indication of a return transaction from point of sale system502, system200may flag the return transaction as potentially fraudulent, since although clerk515appears to be assisting customer525with the return transaction, the absence of another customer proximate to clerk510located at point of sale system501is suspicious. Thus, this situation is flagged for review by the store owner for possible fraud by the clerks510and515. For example, system200could provide a report and/or the video data206captured at the time of the return transaction for review by a manager. Finally, if system200receives two indications of two separate return transactions from both point of sale systems501and502, system200would flag one or both of the return transactions as potentially fraudulent. In particular, the return transaction at point of sale system501is suspicious since no customer is standing proximate to clerk510and point of sale system501to explain why a return transaction might have been initiated at point of sale system501. Thus, any return transaction at point of sale system501in the scenario depicted inFIG. 5would definitely be flagged by system200as potentially fraudulent, and return transactions at point of sale system502in this scenario would also be suspicious and could also be flagged as fraudulent for later review.

FIG. 6is a block diagram illustrating a physical area600to be analyzed by the system200ofFIG. 2according to an embodiment of the invention. The physical area600includes a return area620, point of sale systems601-604, and person610located therein. Note that inFIG. 6no person is located within return area620. Return area620can be designated in a number of ways so that system200is able to determine the area within physical area600that comprises return area620.

In the example ofFIG. 6, person610is standing behind point of sale system601, while no person is located within return area620. System200receives an indication of a return transaction initiated on point of sale system602. Based on the relative position of person610proximate to point of sale system602, system200determines that person610is likely a store clerk operating point of sale system602to effectuate a return transaction. Since no other person is located within physical area600, and in particular since no person is located within return area620that could be a customer requesting a legitimate return transaction, system200determines that the return transaction initiated on point of sale system602is likely fraudulent. Thus, system200flags the return transaction initiated on point of sale system602as potentially fraudulent, so that a manager or store owner can later review the details of the transaction and view the corresponding video that triggered the flag.

FIG. 7is a block diagram depicting a physical area700to be analyzed by system200ofFIG. 2according to another embodiment of the invention, showing a number of items therein and indicating movement of people throughout the area. In particular, physical area700is separated by three aisles720,730, and740. Each aisle includes a number of items; aisle720includes items721-726, aisle730includes items731-736, and aisle740includes items741-746. Proximate to aisle720are people702,704, and706. Person702is pushing shopping cart708. The items721-726,731-736, and741-746ofFIG. 7are organized into respective rows or aisles720,730, and740, as might be done in a grocery store or other retail environment. However, any arrangement of the items721-726,731-736, and741-746, including an arbitrary or randomized placement of the items31721-726,731-736, and741-746within the physical area700may be encountered in other embodiments.

Within the physical area700ofFIG. 7a person702is depicted moving among the items721-726,731-736, and741-746. The person702is accompanied by persons704and706, who are smaller in scale and stature than person702. The person702may be a customer of a grocery store or department store viewing a number of the items721-726,731-736, and741-746for possible purchase. Typically, the person702may travel among the items721-726,731-736, and741-746, potentially selecting one or more of the items721-726,731-736, and741-746for purchase. As described in greater detail below, this movement of the user702and the selection of one or more of the items721-726,731-736, and741-746may be utilized in determining a conversion rate statistic for a particular store, a particular department within a store, a particular shelf or aisle720,730, or740, or even a particular item721-726,731-736, and741-746.

In the environment ofFIG. 7, one or more of the video cameras210portrayedFIG. 2may capture or generate video206of the persons702,704, and706as they move through the physical area700among the items721-726,731-736, and741-746. The cameras210may be located and oriented to provide sufficient coverage of the physical area700to capture the movement of the people702,704, and706among the items721-726,731-736, and741-746located in the area700.

The cameras210then transfer the video206to the processing system204of the system200via the communication interface202. The processing system204analyzes the video206to determine the movement of each person702,704, and706within the physical area700. In one example, the video206is composed of a series of images, or frames, of some or all of the physical area700spanning a period of time. In that context, the processor204may employ software configured to detect humans, or a particular feature of humans, such as a head, to detect each person702,704, and706within each of the frames. This detection allows the processing system204to track the movement of the people702,704, and706within the physical area700in relation to the items721-726,731-736, and741-746. In one implementation, the processing system204may describe the movement in terms of speed and direction of the persons702,704, and706at various points in time.

As opposed to using detection of the human form in the video206, the processing system204may instead detect an object closely identified with a person in the physical area700. In the example ofFIG. 7, the person702is shown pushing a shopping cart708, such as that which may be found in a grocery store or similar establishment. Since the person702is likely to be located near the cart708throughout the majority of the time the person702is within the store, the software for determining the movement of the person702may be configured to detect the movement of the cart708as a proxy for the movement of the corresponding person702. The processing system204may apply the same methodology to track similar containers, such as a hand basket or shopping bag. In other implementations, other objects associated with the person702during movement within the physical area700may include vehicles, such as motorized shopping carts, cars, golf carts, and so on, used by the person702to travel within the area700.

The processing system204may analyze the video206to yield the movement of the people702,704, and706within the physical area700in real-time as the video206is received by the communication interface202from the cameras210. In another example, the processing system204may store the video206in a data storage system (not shown inFIG. 2) prior to analyzing the video206in a post-processing batch mode.

In the example ofFIG. 7, conversion rate statistics can be determined by counting the number of people702,704, and706entering a designated area700or a particular aisle720,730, and740within the area700, and correlating this information to actual sales of items721-726,731-736, and741-746. The sales figures of items721-726,731-736, and741-746could be provided to system200via point of sale systems222and223ofFIG. 2over communication link225. In this example, physical area700could comprise a single department of a retail establishment, or could comprise an entire sales floor of the retail establishment. If the physical area is an entire sales area of a retail establishment, certain sections of the area700could be designated as departments and analyzed separately to determine conversion rate statistics per department.

InFIG. 7, system200could group persons702,704, and706together as a single buying unit when determining conversion rate statistics. For example, system200could determine that, based on the smaller size and stature of persons704and706, and that the larger person702is pushing the shopping cart708, persons704and706are likely small children who are shopping with a parent702. Thus, for purposes of calculating a conversion rate based on the items purchased by persons702,704, and706, the persons702,704, and706could be treated as a single person702. This assumption could be altered if system200later receives video of each person702,704, and706purchasing items separately when checking out at point of sale systems222and223.

The persons702,704, and706are tracked by system200in a number of ways. In one example, facial recognition could be used to associate particular facial features of each person702,704, and706with that respective person702,704, and706. In this manner, the facial information of each person702,704, and706could be stored for later use by system200, thus allowing the gathering of data regarding the movement of the people702,704, and706over any time period such as days, weeks, or years, and including subsequent visits to the area700. In this manner, the buying habits of persons702,704, and706could also be tracked by system200.

In another example, system200may identify particular items721-726,731-736, and741-746that persons702,704, and706select and carry throughout the physical area700prior to purchasing the items721-726,731-736, and741-746. In the example ofFIG. 7, persons702,704, and706have selected item723in aisle720, as shown inFIG. 7by the darker gray shading of item723. System200could therefore track the persons702,704, and706by the presence of item723in the shopping cart708, and correlate the purchase of this item723by persons702,704, and706when they leave the physical area700and check out at a point of sale system222or223as shown inFIG. 2. In other examples, system200could also be configured to track shoplifters who select an item721-726,731-736, and741-746but do not purchase the item upon leaving the physical area700.

Once the data is gathered by system200related to persons702,704, and706who enter particular aisles720,730, and740or departments of physical area700, system200could calculate a conversion rate statistic for the entire retail establishment, per department of the retail establishment, per aisle720,730, or740, or per item721-726,731-736, and741-746. For example, system200could calculate the conversion rate of item723by treating persons702,704, and706as a single buying unit, and therefore the rate is one to one, because one item723was purchased by the single buying unit of persons702,704, and706. Typically, a conversion rate is expressed as a percentage of sales relative to a number of shoppers, so in this case, the conversion rate of item723is 100% when treating persons702,704, and706as a single buying unit.

Based on the conversion rate statistics determined by system200, retailers may determine which products are selling well, and which areas of the store are producing the most sales per shopper. In addition, proprietors can identify more desirable or effective product display locations within a retail establishment that may command greater advertising revenue. For example, certain locations within a store, such as the ends of a product aisle, shelf space approximately aligned at eye-level, and display space near a checkout stand, may attract more customer attention, and hence are likely to produce higher conversion rates for items placed thereon, and this assumption could be verified by the actual conversion rates determined by system200. Accordingly, the retailers could charge a manufacturer, distributor, or other supplier a higher fee to display products at locations within the store that result in higher conversion rates.

While some higher-profile locations within a retail environment are generally identifiable from past experience, others may be determined by conversion rate statistics of a specific area of a retail establishment, such as a particular aisle720,730, or740, a particular department within the establishment, or even a particular item721-726,731-736, and741-746for sale within the retail establishment. Based on these more granular conversion rate statistics, the retailer may then allocate or apportion a greater portion of the advertising or display costs to suppliers of products that are located along the identified areas.

FIG. 8illustrates a block diagram of a computer system800. Computer system800provides an example of the system200ofFIG. 2, although system200could use alternative configurations. Computer system800includes communication interface801, user interface802, and processing system803. Processing system803is linked to communication interface801and user interface802. Processing system803includes processing circuitry805and memory system806that stores operating software807. Operating software807includes processing instructions to be executed by the processing system803to perform the various functions described above for system200. Computer system800could include a programmed general-purpose computer, although those skilled in the art will appreciate that programmable or special purpose circuitry and equipment may be used. Computer system800may be distributed among multiple devices that together include elements801-807.

Communication interface801comprises components that communicate over communication links, such as network cards, transceivers, ports, RF transceivers, processing circuitry and software, or some other communication components. Communication interface801may be configured to communicate over metallic, wireless, or optical links. Communication interface801may be configured to use time-division multiplexing (TDM), Internet Protocol (IP), Ethernet, optical networking, wireless protocols, communication signaling, or some other communication format—including combinations thereof. In some examples, communication interface801could include a network interface, modem, port, transceiver, or some other communication device, as well as the communication interface202ofFIG. 2for receiving video information206as described above. Communication interface801may be distributed among multiple communication devices. Communication interface801is configured to receive an indication of a return transaction from a point of sale system, wherein the indication of the return transaction indicates a time when the return transaction occurred. Communication interface801is further configured to receive video data of an area proximate to the point of sale system captured at the time when the return transaction occurred.

User interface802comprises components that interact with a user to receive user inputs and to present media and/or information. User interface802may include a speaker, microphone, buttons, lights, display screen, touch screen, touch pad, scroll wheel, communication port, or some other user input/output apparatus—including combinations thereof. User interface802could also include a keyboard, mouse, voice recognition interface, graphical display, or some other type of user device. User interface802could be distributed among multiple user devices. User interface802may be omitted in some examples.

Processing circuitry805comprises a microprocessor, logic circuits, or other circuitry that retrieves and executes operating software807from memory system806. Processing circuitry805may comprise a single device or could be distributed across multiple devices—including devices in different geographic areas. Processing circuitry805may be embedded in various types of equipment. Memory system806comprises a non-transitory computer readable storage medium, such as a disk drive, flash drive, data storage circuitry, or some other hardware memory apparatus. Memory system806may comprise a single device or could be distributed across multiple devices—including devices in different geographic areas. Memory system806may be embedded in various types of equipment. Operating software807comprises computer programs, firmware, or some other form of machine-readable processing instructions. Operating software807may include an operating system, utilities, drivers, network interfaces, applications, or some other type of software.

When executed by circuitry805, operating software807directs processing system803to operate as described herein for system200with respect toFIGS. 1-7. In particular, operating software807may direct processing system803to direct communication interface801to receive an indication of a return transaction from a point of sale system, wherein the indication of the return transaction indicates a time when the return transaction occurred. Further, operating software807may direct processing system803to direct communication interface801to receive video data of an area proximate to the point of sale system captured at the time when the return transaction occurred. In addition, operating software807may direct processing system803to process the video data to determine whether a first person is located in a return area and whether a second person is located proximate to the point of sale system. Operating software807may direct processing system803to determine that the return transaction is legitimate if the first person is located in the return area and the second person is located proximate to the point of sale system, or if the first person is located in the return area, no person is located proximate to the point of sale system, and the second person is located proximate to a different point of sale system. Finally, operating software807may direct processing system803to flag the return transaction as potentially fraudulent if the first person is located in the return area, the second person is located proximate to the different point of sale system, and a third person is located proximate to the point of sale system, flagging the return transaction as potentially fraudulent, or if the first person is not located in the return area and the second person is located proximate to the point of sale system.