Abstract:
Transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 13/559,996, filed Jul. 27, 2012, which is a continuation of U.S. application Ser. No. 12/697,530, filed Feb. 1, 2010, now U.S. Pat. No. 8,259,175 B2, issued Sep. 4, 2012. 
    
    
     FIELD OF THE INVENTION 
     The present invention generally relates to video surveillance, and more particularly relates to using a computer infrastructure to prioritize the processing of multiple video streams. 
     BACKGROUND 
     Video surveillance in a retail environment is a common practice. However, it remains resource intensive to process captured video to automatically detect irregular activities. In retail environment, in order to automatically capture irregular activities such as cashier frauds at check-out lanes, sophisticated and resource intensive computerized pattern recognition algorithms need to be executed. By multiplying by the scale of the lanes (10˜20 each store and thousands nationwide), a significant amount of computational power is required to handle the huge volume of output generated by complex computer processing. 
     In addition, each store usually has only limited space and resources to handle all point-of-sale (POS) transactions and associated video streams. Available space may be sufficient for smaller stores that have fewer lanes, but it is not sufficient for larger stores with 15˜20 lanes, or even more. At the same time, retailers are not always willing to invest more into the hardware, software and services necessary to keep up with the need. 
     As a result, if the available computational resources cannot keep up with the need, useful information will be dropped due to this shortage, e.g., frames are dropped in the video streams and/or processing is limited to only a subset of video streams. This may cause many irregular activities to be missed, resulting in severe loss to the retailers. 
     SUMMARY 
     In one method aspect of the present invention, transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units. 
     In another aspect of the present invention, a computer program product includes a computer-readable storage medium having computer-readable program code embodied in the storage medium. The computer readable program code includes instructions that, when executed by a processor, cause the processor to prioritize transaction units of video data and transaction data captured from different checkout lanes as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units. 
     In another aspect of the present invention, a system has a memory and at least one processor coupled to the memory and operative to determine processing priority for each transaction unit of individual transaction units including video and transaction data. More particularly, transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes that the transaction units are acquired from. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       These and other features of the invention will be more readily understood from the following detailed description of the various aspects of the invention taken in conjunction with the accompanying drawings that depict various aspects of the invention, in which: 
         FIG. 1  shows an illustrative environment for a system for prioritizing multiple video streams processing according to an aspect of the invention. 
         FIG. 2  shows a close up of an illustrative environment for prioritizing multiple video streams processing according to an aspect of the invention. 
         FIG. 3  illustrates a system diagram of an exemplary intelligent switching program according to an aspect of the invention. 
         FIGS. 4A and 4B  illustrate examples of calculating priorities for different transaction units. 
         FIG. 5  illustrates a flowchart for exemplary steps of prioritizing multiple video streams processing according to an aspect of the invention. 
         FIG. 6  illustrates a flowchart for exemplary steps of assigning priorities for multiple video streams. 
     
    
    
     It is noted that the drawings are not to scale. The drawings are intended to depict only typical aspects of the invention, and therefore should not be considered as limiting the scope of the invention. In the drawings, like numbering represents like elements between the drawings. 
     DETAILED DESCRIPTION 
     The present invention generally relates to video surveillance, and more particularly relates to using a computer infrastructure to prioritize the processing of multiple video streams in a retail environment. 
     Aspects of the invention aim to address the scalability issue encountered in retail stores, where computational power is often insufficient to monitor all check-out lanes simultaneously for irregular activities such as cashier frauds, particularly during periods of high activity (e.g., during the holiday shopping season). 
     Aspects of the invention involve implementation of an intelligent switching program, whereby the processing power required to monitor check-out stations is considerably reduced. In an aspect, the present invention monitors a subset of check-out stations at any given time, instead of monitoring all check-out stations at all times. The subset of check-out stations may be determined dynamically according to, but not limited to, cashier records, input parameters from the manager, current lane activity, past lane activity, time of day, etc. Statistical models, e.g., effective population sampling and/or population hypothesis tests, are developed based on the above variables to guide the lane selection process, whereby increases in the false-negative rate due to failure to monitor particular lanes when events of interest occur are controlled. By monitoring fewer check-out stations, while maintaining target performance accuracy, the amount of data that end users must deal with is significantly reduced. 
     According to an aspect of the invention, it is assumed that there are N check-out lanes to monitor and a single processing machine equipped with an irregular activity capture module. During any unit time period, e.g., 10 seconds, the system may be able to process a desired number of transactions. Thus, the present invention develops an intelligent switching program for lane selection and dynamically allocates processing power to different lanes from time to time. The system is also capable of dynamically adjusting its allocation based on real-time incoming data. 
     In an aspect of the invention, the processing power can be located at a different location from the check-out lanes and can monitor check-out lanes from more than one store at various locations. The processing power can also process historical data along with real-time data. 
     According to an aspect of the invention, there may be shared computational resources among different retail stores. For example, a regional or national processing center may provide backup to any overloaded individual store. In this case, each store initially has its own scheduling and prioritization procedures to handle its own transactions. If there are transactions with high priorities that cannot be handled by local computational resources, a request will be sent to the regional or national processing center to process the load. Since different stores may have different problem definitions, the higher level processing unit does not necessarily contain the same analytic modules as individual stores do. 
     The processing unit at the regional or national processing center may just provide the computational power, while what to compute is defined by the requests sent by the individual stores. The requests sent by the individual stores include the transaction data, video streams and the task definitions. Transaction data refers to data from POS devices including customer number, prices, item numbers, quantities, discounts, voids, etc. The processing units could reside in the same physical location, or they could be in distributed form and be referenced by their virtual/logical addresses. 
     Further aspects of the present invention provide an open architecture to integrate processing from different locations as well as different retailers. When the computational resource of one vendor is limited, a higher-level processing unit could allocate free resources from another vendor to assume the burden. 
     In prioritizing the processing of multiple video streams, the processing power may rely on a set of initial rules that are capable of being dynamically updated. The initial input of the monitoring system may include, but is not limited to: user preference, e.g., Lane  10  is considered sensitive and so should have more focus than other lanes; more focus should be placed on a particular cashier when he or she is on duty; historical data: e.g., the transaction volume on past Sundays, usual time of day, date, day of the week, etc. A set of statistical sampling and population estimation techniques (e.g., hypothesis testing) are employed to further enforce the confidence of the prioritization process. 
     Based on the initial system input described above, the selective monitoring unit may initiate a statistical sampling process to allocate computational resources, such that the lanes or the lanes occupied by certain cashiers receive more focus than others. The sampling process is based on statistical inference techniques with context-aware (retail) prior information and mathematical models. 
     In an aspect of the invention, as the system continues to be provided with new information, it can dynamically adjust its computational resource allocation. To maintain the target capture accuracy, the intelligent switching program may adjust its focus to lanes with a higher processing rate. The system should have a different profile for different time periods during a day. This could be pre-defined as the initial input. In addition, the volume of particular types of transactions can trigger the intelligent switching program to change focus. For instance, if one lane produces more “void transaction” events than others, the system may adjust its focus to process more transactions from this lane. In other words, if a lane/cashier produces more “candidate” irregular activities such as cashier frauds, the system may put more focus on the lane and/or cashier. 
     In an aspect of the invention, the event triggers for the intelligent switching program to switch focus may not be evaluated independently from each other. Rather, they may be modeled as a joint distribution as there may be a strong correlation among them. Common feature models may be used, such as Gaussian, Poisson, exponential, uniform, etc. 
     Hypothesis tests and statistical sampling processes are carefully designed such that the target irregular activity capture accuracy is maintained, e.g., how many items from a lane and/or a cashier the system should process to maintain a 75% capture rate. This is highly context related, and standard statistical methods are modified to fit an application. 
     In addition, a prescheduling module determines whether a particular lane should be monitored at any given time based on whether the lane is open. Prior to the processing by the intelligent switching program to prioritize the processing of the video streams, some preprocessing is performed by a preprocessing module on all lanes to produce intermediate transactions. These intermediate transactions provide transaction units for further processing. A transaction unit contains transaction video which corresponds to a set of items purchased by a single customer in a single span of time. 
     The intermediate features along with prior information are used to decide which transactions should receive prioritized processing (e.g., to catch cashier irregular activity). Results are archived for human perusal and validation. 
     Turning to the drawings,  FIG. 1  shows an illustrative environment for prioritizing the processing of multiple video streams according to an aspect of the invention. To this extent, at least one camera  42  captures activities in a checkout lane. Camera  44  and camera  46  each capture activities in a different checkout lane. Accordingly, a digital video input  41  from camera  42 , a digital video input  43  from camera  44 , a digital video input N from camera  46  are obtained and sent to a system  12  that includes, for example, an intelligent switching program  30 , data  50 , parameters  52 , output  54  and/or the like, as discussed herein. Transaction data  47 ,  48 , and M from the each of the checkout lanes are sent to system  12  to be processed. 
       FIG. 2  shows a closer view of an illustrative environment  10  for prioritizing the processing of multiple video streams according to an aspect of the invention. To this extent, environment  10  includes a computer system  12  that can perform the process described herein in order to detect irregular checkout activities. In particular, computer system  12  is shown including a computing device  14  that includes an intelligent switching program  30 , which makes computing device  14  operable for prioritizing the processing of multiple video streams, by performing the process described herein. 
     Computing device  14  is shown including a processor  20 , a memory  22 A, an input/output (I/O) interface  24 , and a bus  26 . Further, computing device  14  is shown in communication with an external I/O device/resource  28  and a storage device  22 B. In general, processor  20  executes program code, such as intelligent switching program  30 , which is stored in a storage system, such as memory  22 A and/or storage device  22 B. While executing program code, processor  20  can read and/or write data, such as data  36  to/from memory  22 A, storage device  22 B, and/or I/O interface  24 . Bus  26  provides a communications link between each of the components in computing device  14 . I/O device  28  can include any device that transfers information between a user  16  and computing device  14  and/or digital video input  41 ,  43 , N and transaction data input  47 ,  48 , M and computing device  14 . To this extent, I/O device  28  can include a user I/O device to enable an individual user  16  to interact with computing device  14  and/or a communications device to enable an element, such as digital video input  41 ,  43 , N and transaction data input  47 ,  48 , M to communicate with computing device  14  using any type of communications link. 
     In any event, computing device  14  can include any general purpose computing article of manufacture capable of executing program code installed thereon. However, it is understood that computing device  14  and intelligent switching program  30  are only representative of various possible equivalent computing devices that may perform the process described herein. To this extent, in other aspects, the functionality provided by computing device  14  and intelligent switching program  30  can be implemented by a computing article of manufacture that includes any combination of general and/or specific purpose hardware and/or program code. In each aspect, the program code and hardware can be created using standard programming and engineering techniques, respectively. Such standard programming and engineering techniques include an open architecture to allow integration of processing from different retailers. Such an open architecture includes cloud computing. 
     Similarly, computer system  12  is only illustrative of various types of computer systems for implementing aspects of the invention. For example, in one aspect, computer system  12  includes two or more computing devices that communicate over any type of communications link, such as a network, a shared memory, or the like, to perform the process described herein. Further, while performing the process described herein, one or more computing devices in computer system  12  can communicate with one or more other computing devices external to computer system  12  using any type of communications link. In either case, the communications link can include any combination of various types of wired and/or wireless links; include any combination of one or more types of networks; and/or utilize any combination of various types of transmission techniques and protocols. 
     As discussed herein, intelligent switching program  30  enables computer system  12  to detect irregular checkout activities. To this extent, intelligent switching program  30  is shown including a prescheduling module  32 , a preprocessing module  34 , a prioritizing module  36 , a processing module  37 , a cleanup module  38 , and an archiving module  39 . Operation of each of these modules is discussed further herein. However, it is understood that some of the various modules shown in  FIG. 2  can be implemented independently, combined, and/or stored in memory of one or more separate computing devices that are included in computer system  12 . Further, it is understood that some of the modules and/or functionality may not be implemented, or additional modules and/or functionality may be included as part of computer system  12 . 
       FIG. 3  illustrates a system diagram of an exemplary video transaction intelligent switching program  30  ( FIG. 2 ). The present invention contemplates a plurality of lanes in one or more retail stores. This non-limiting example depicts a system prioritizing the processing of multiple video streams installed in a retail store. To this extent, the retail store maintains an arbitrary number of lanes. Cameras are installed to capture transaction activities at each lane. Transaction data (e.g., prices, item numbers, quantities, etc.) is sent along with video capture of each transaction. The transaction includes both the transaction data and the video stream. This non-limiting example assumes that there are N lanes (lane  1  to lane N) to be processed as determined by a prescheduling module  32  ( FIG. 2 ). In the prescheduling module, a prescheduling filter  111  is installed for lane  1 . Likewise, another filter  112  is installed for lane  2 , and filter  113  is installed for lane N−1, and filter  114  for lane N. 
     The prescheduling filters determine whether a transaction from a particular lane should be monitored based on whether the lane is open. All transactions from lanes are sent to the preprocessing module  34  ( FIG. 2 ). The preprocessing module  34  organizes the transactions such that each transaction is isolated and given a unique ID. All transactions are then presented to the prioritizing module  36 . The prioritizing module  36  uses predetermined rules, which are also capable of being dynamically updated, to calculate a priority score for each transaction. 
     The prioritizing module  36  maintains a transaction priority queue  140 , which contains transactions with priority scores. Transactions are listed in the transaction priority queue  140  in the order of their priority score. The processing module  37  processes transactions with the highest priority score first from the transaction priority queue  140 . The processing module  37  contains the relatively computationally-intensive irregular activity detection software to analyze each transaction to discover whether irregular activity has occurred for that particular transaction. As a transaction is processed by the processing module  37 , the transaction unit for that transaction is moved to an archival queue  170 . 
     A cleanup module  38  monitors the transaction priority queue  140  at regular time intervals. If a transaction has been in the transaction priority queue  140  for more than a predetermined amount of time (e.g., 10 seconds) and the priority score for the transaction is low, the cleanup module  38  will move the transaction to the archival queue  170 . 
     The archiving module  39  processes transaction units in the archival queue  170  by moving the transaction units in the archival queue  170  to persistent storage  190 . From persistent storage  190 , data can be extracted to form part of prioritizing rules  195 . Human operator  16  can also provide prioritizing rules  195 . Prioritizing rules  195  are used by the prioritizing module  36  to prioritize transactions. 
     According to an aspect of the invention, when the processing module  37  has unused capacity (e.g., late at night when there are few customers or when the store is closed), unprocessed transactions from the persistent storage  190  can be sent back to the prioritizing module  36  to be reprocessed 
       FIGS. 4A and 4B  illustrate two stages of priority setting among transactions.  FIG. 4A  illustrates a first stage of prioritizing transactions. In this first stage, transactions from Lanes  1 - 3  are sent to the prioritizing module  36 . Based on the prioritizing rules provided by human operator  16  and the characteristics of each transaction, the prioritizing module  36  prioritizes transactions in the order of T 1  to T N  with T 1  having the highest priority. 
     For example, T 1  from Lane  1  has the highest priority because the cashier operating Lane  1  has been flagged by the manager. T 2  from Lane  2  is given a high priority because the transaction contains three voided items. T 3  from Lane  3  is also given a high priority because the entire transaction is voided. However, the priority given to T 2  is higher than that of T 3  because three voided items in a single transaction is considered a more irregular activity than voiding an entire transaction, according to the system rule design. T 4  from Lane  2  is given a high priority because the transaction contains unusually long durations between item scans. Long durations between scans is a possible cue that the cashier is moving items from the entry belt to the exit belt without entering the items into the transaction between items that are being entered into the transaction (i.e., the items are bagged and taken away by the customer without being purchased). There are many other reasons for long durations between items (e.g., the cashier stops to bag items), so T 4  is given a lower priority than T 1 -T 3 . In comparison, T N  from Lane  2  is given low priority because it is a seemingly ordinary transaction. 
       FIG. 4B  illustrates a second stage of prioritizing transactions. In this second stage, the prioritizing rules have been updated with the transaction data from the first stage. In the first stage, the cashier from Lane  2  issued multiple suspicious transactions. As a result, the prioritizing rules were updated based on this information. 
     In the second stage as illustrated by  FIG. 4B , new transactions T 1  to T N  are processed by the prioritizing module  36 . T 1  from Lane  2  is given top priority because the first stage showed multiple suspicious transactions from the same cashier and a large number of high value items. T 2  from Lane  3  is given a high priority because the same cashier from Lane  3  had two managerial overwrites issued. T 3  from Lane  3  is given high priority because the same cashier is considered suspicious. T 4  is given high priority because a customer pays cash. Paying in cash means that the identity of buyer is not recorded as it would be, for example, in a credit card transaction, so there is a correlation between cash purchases and fraud. However, the correlation is not very strong relative to the items that are prioritized ahead of it. T N  is given low priority because a different cashier now works at Lane  1  and the cashier is not flagged as suspicious. In addition, the transaction T N  is an ordinary, non-suspicious transaction. 
       FIG. 5  illustrates a flowchart for exemplary steps of assigning priorities for transaction units. In step  501 , the intelligent switching program takes initial input for assigning priorities. In step  502 , the intelligent switching program initiates a statistical sampling model to process transactional data and video stream, which is combined into a transaction unit. In step  503 , the intelligent switching program analyzes features of each transaction unit. The features includes but are not limited to: activity level at each lane, e.g., can be obtained by analyzing the object detection and tracking algorithms; volume of transactions in terms of both number of transactions and monetary amounts; and results of irregular activity detections in the near history which is also used to update the historical data to affect future transaction priority ordering. In step  504 , the intelligent system uses statistical models (e.g., Gaussian, Poisson, exponential, uniform, etc.) to determine correlations between features of the transaction units. In step  505 , each transaction unit is given a priority score based on analysis results and placed in the transaction priority queue. 
       FIG. 6  illustrates a flowchart for exemplary steps of processing transactions according to the present invention. In step  601 , the monitoring system preprocesses video capture and transaction data and turns them into identifiable individual transaction units. In step  602 , the prioritizing system prioritizes the individual transaction units based on priority rules. In step  603 , the system determines whether a transaction unit has high enough priority to be processed. If the transaction unit has relatively high priority, the transaction is processed in step  604 . If the transaction unit does not have high priority in step  603 , the transaction unit is archived directly in step  605 . After the transaction unit is processed in step  604 , irregular activities are captured and reported in step  606 . Processed transactions from step  604  are also archived in step  605 . An analysis of all transactions from step  605  provides the basis for updating prioritizing rules in step  607 .