1. 1 Activity and Motion Detection in Videos Longin Jan Latecki and Roland Miezianko, Temple University Dragoljub Pokrajac, Delaware State University Dover, August 2005

2. Definition of Motion Detection Action of sensing physical movement in a give area Motion can be detected by measuring change in speed or vector of an object 2

3. 3 Motion Detection Goals of motion detection Identify moving objects Detection of unusual activity patterns Computing trajectories of moving objects Applications of motion detection Indoor/outdoor security Real time crime detection Traffic monitoring Many intelligent video analysis systems are based on motion detection.

4. 4 Two Approaches to Motion Detection Optical Flow –Compute motion within region or the frame as a whole Change detection –Detect objects within a scene –Track object across a number of frames

5. 5 Background Subtraction Uses a reference background image for comparison purposes. Current image (containing target object) is compared to reference image pixel by pixel. Places where there are differences are detected and classified as moving objects. Motivation: simple difference of two images shows moving objects

6. 6 a. Original scene b. Same scene later Subtraction of scene a from scene bSubtracted image with threshold of 100

7. 7 Static Scene Object Detection and Tracking Model the background and subtract to obtain object mask Filter to remove noise Group adjacent pixels to obtain objects Track objects between frames to develop trajectories

8. 8 Background Modelling by Michael Knowles

9. 9 Background Model

10. 10 After Background Filtering…

11. 11 Approaches to Background Modeling Background Subtraction Statistical Methods (e.g., Gaussian Mixture Model, Stauffer and Grimson 2000) Background Subtraction: 1.Construct a background image B as average of few images 2.For each actual frame I, classify individual pixels as foreground if |B-I| > T (threshold) 3.Clean noisy pixels

12. 12

13. 13 Background Subtraction Background Image Current Image

14. 14 Statistical Methods Pixel statistics: average and standard deviation of color and gray level values (e.g., W4 by Haritaoglu, Harwood, and Davis 2000) Gaussian Mixture Model (e.g., Stauffer and Grimson 2000)

15. 15 Gaussian Mixture Model Model the color values of a particular pixel as a mixture of Gaussians Multiple adaptive Gaussians are necessary to cope with acquisition noise, lighting changes, etc. Pixel values that do not fit the background distributions (Mahalanobis distance) are considered foreground

16. 16 Gaussian Mixture Model Block 44x42 Pixel 172x165 R-G DistributionR-G-B Distribution

17. VIDEO 17

18. 18 Proposed Approach Measuring Texture Change Classical approaches to motion detection are based on background subtraction, i.e., a model of background image is computed, e.g., Stauffer and Grimson (2000) Our approach does not model any background image. We estimate the speed of texture change.

19. 19 In our system we divide video plane in disjoint blocks (4x4 pixels), and compute motion measure for each block. mm(x,y,t) for a given block location (x,y) is a function of t

20. 20 8x8 Blocks

21. 21 Block size relative to image size Block 24x28 1728 blocks per frame Image Size: 36x48 blocks

22. 22 Motion Measure Computation We use spatial-temporal blocks to represent videos Each block consists of N BLOCK x N BLOCK pixels from 3 consecutive frames Those pixel values are reduced to K principal components using PCA (Kahrunen-Loeve trans.) In our applications, N BLOCK =4, K=10 Thus, we project 48 gray level values to a texture vector with 10 PCA components

23. 23 3D Block Projection with PCA (Kahrunen-Loeve trans.) 48-component block vector (4*4*3) -0.5221 -0.0624 -0.1734 -0.2221 -0.2621 -0.4739 -0.4201 -0.4224 -0.0734 -0.1386 10 principal components t+1 t t-1 4*4*3 spatial-temporal block Location I=24, J=28, time t-1, t, t+1 Motion Measure Computation

24. 24 Texture of spatiotemporal blocks works better than color pixel values More robust Faster We illustrate this with texture trajectories.

25. 25 499624 8631477

26. 26 Trajectory of block (24,8) (Campus 1 video) Space of spatiotemporal block vectors Moving blocks corresponds to regions of high local variance, i.e., higher spread

27. 27 Campus 1 video block I=24, J=28 Standardized PCA components of RGB pixel values at pixel location (185,217) that is inside of block (24,28). Comparison to the trajectory of a pixel inside block (24,8)

28. 28 Detection of Moving Objects Based on Local Variation For each block location (x,y) in the video plane Consider texture vectors in a symmetric window [t-W, t+W] at time t Compute the covariance matrix Motion measure is defined as the largest eigenvalue of the covariance matrix

29. 29 4.2000 3.5000 2.6000 4.1000 3.7000 2.8000 3.9000 3.9000 2.9000 4.0000 4.0000 3.0000 4.1000 3.9000 2.8000 4.2000 3.8000 2.7000 4.3000 3.7000 2.6500 Feature vectors 0.0089 -0.0120 -0.0096 -0.0120 0.0299 0.0201 -0.0096 0.0201 0.0157 Covariance matrix Feature Vectors in Space 0.0499 0.0035 0.0011 Eigenvalues 0.0499 Motion Measure Current time

30. 30 4.3000 3.7000 2.6500 4.4191 3.5944 2.4329 4.1798 3.8415 2.6441 4.2980 3.6195 2.5489 4.2843 3.7529 2.7114 4.1396 3.7219 2.7008 4.3257 3.6078 2.8192 Feature vectors 0.0087 -0.0063 -0.0051 -0.0063 0.0081 0.0031 -0.0051 0.0031 0.0154 Covariance matrix Feature Vectors in Space 0.0209 0.0093 0.0020 Eigenvalues 0.0209 Motion Measure Current time

31. 31 Graph of motion measure mm(24,8,:) for Campus 1 video

32. 32 Graph of motion measure mm(40,66) of Sub_IR_2 video Motion MeasureDetected Motion

33. 33 Dynamic Distribution Learning and Outlier Detection (1) (2) (3) (4) (5) Detect Outlier Switch to a nominal state Update the estimates of mean and standard deviation only when the outliers are not detected