System and method for process segmentation using motion detection

• US 20080122926A1
• Filed: 08/14/2006
• Published: 05/29/2008
• Est. Priority Date: 08/14/2006
• Status: Abandoned Application

• Current Assignee: Fuji Xerox Company Limited (Fujifilm Holdings Corporation)
• Original Assignee: Fuji Xerox Company Limited (Fujifilm Holdings Corporation)
• Inventors: Turner, Althea, Zhou, Hanning, Kimber, Donald
• Application Number:
  US11/504,277
• Publication Number:
  US 20080122926A1
• Time in Patent Office: Days
• US Class Current: 348/143
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 7/215 Motion-based segmentation
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources

Abstract

Video recording technology is utilized to enable business process investigation in an unobtrusive manner. Several cameras are situated, each having a defined field of view. For each camera, a region of interest (ROI) within the field of view is defined, and a background image is determined for each ROI. Motion within the ROI is detected by comparing each frame to the background image. The video recording can then be segmented and indexed according to the motion detection.

Claims

1. A method for analyzing process flow, comprising:

determining physical areas affected by the process flow;

generating a video recording using least one video camera having a field of view covering said physical area;

designating at least one region of interest (ROI) in the field of view of said video recording;

determining a background image in said ROI;

segmenting said video recording into process segment sessions by detecting motion in said ROI, each of said segments beginning upon detection of motion and ending upon cessation of motion.

2. The method of claim 1, wherein said detecting motion comprises combining multiple features depicting difference between said background image and current frame.

3. The method of claim 2, wherein motions detected at multiple cameras are combined into a single event.

4. The method of claim 2, wherein a motion is detected only when said difference is above a preset threshold.

5. The method of claim 1, wherein said detecting motion comprises applying the sum of absolute difference filter to a hue channel of said video recording.

6. The method of claim 5, wherein said sum of absolute difference is weighted in correspondence with saturation value of said video recording.

7. The method of claim 1, wherein said detecting motion comprises detecting normalized correlation between the background image and a current image of said video recording.

8. The method of claim 1, further comprising indexing the segment sessions.

9. The method of claim 1, further comprising generating a trace of trajectory of each detected motion.

10. The method of claim 9, wherein said trace is generated using combined motion detected at a plurality of cameras.

11. The method of claim 9, wherein generated traces are clustered according to defined parameters.

12. The method of claim 11, wherein the parameters are selected from area of motion, frequency of motion, speed of motion, time of day of the motion.

13. The method of claim 1, wherein said detecting motion comprises combining results provided by applying sum of absolute difference (SAD), Lucas-Kanade Optical Flow (LKF), and Normalized Correlation (NC) analyses to the video recording.

14. The method of claim 13, wherein combining the results comprises applying supervised learning of a binary classifier process to the results of the SAD, LKF and NC.

15. The method of claim 1, further comprising plotting the number of customers present in said ROI per unit of time.

16. The method of claim 15, further comprising obtaining a ratio of the number of customers per employee per unit of time.

17. The method of claim 1, further comprising plotting the length of time per transaction detected in said ROI.

18. The method of claim 1, further comprising plotting the number of transactions per each length of time of transaction.

19. A system for investigating business process, comprising:

a video monitor;

a processor coupled to the monitor;

a plurality of cameras connected to said processor, each camera having a field of view;

a video driver controlled by said processor to receive video signals from said cameras and display video images on the monitor;

a user interface for defining a region of interest in an image displayed on said monitor;

a memory storing a background image defined within said region of interest;

wherein said processor detects motion in said video signals by comparing frames of said video signals to said background image.

20. The system of claim 19, wherein said processor further segments said video signals to sessions according to detected motion.

21. The system of claim 19, wherein said processor generates a trace of detected motion in said video signals.

22. The system of claim 21, wherein said processor generates the trace of detected motion by combining motions detected in video signals from a plurality of cameras.

23. The system of claim 19, wherein said processor detects motion by applying sum of absolute difference (SAD), Lucas-Kanade Optical Flow (LKF), and Normalized Correlation (NC) analyses to the video signals and combining the results obtained from the SAD, LKF and NC analysis.

24. The system of claim 23, wherein the processor combines the results by applying a supervised learning of a binary classifier process to the results of the SAD, LKF and NC.

25. A method for detecting a motion in a video stream, comprising:

obtaining a video stream;

applying sum of absolute difference (SAD) analyses to the video stream to obtain SAD results;

applying Lucas-Kanade Optical Flow (LKF) analyses to the video stream to obtain LKF results;

applying Normalized Correlation (NC) analyses to the video stream to obtain NC results; and

,

combining the SAD results, the LKF results, and the NC results to obtain motion detection.

26. The method of claim 25, further comprising applying a supervised learning of a binary classifier to the SAD results, the LKF results, and the NC results.