Background modeling with feature blocks

1. A computer-implemented method for video content analysis of a video comprising:

modeling a background of the video, comprising:

dividing each frame of the video into image blocks;

determining features for each image block of each frame to obtain feature blocks for each frame;

determining a feature block map for each frame based on the feature blocks of each frame; and

determining a background feature block map to model the background of the video based on at least one of the feature block maps;

detecting at least one target in a foreground of the video based on the feature blocks of the video; and

tracking each target of the video.

2. The method of claim 1 , wherein determining features for each image block of each frame to obtain feature blocks for each frame comprises:

determining a pixel intensity for each image block;

placing pixels in each image block into a category based on the pixel intensity;

determining a pixel intensity gradient for each image block;

determining pixels in the image block having a high intensity or a low intensity;

counting a number of pixels determined to have the high intensity or low intensity; and

determining the feature based on the pixel intensity gradient and the counting.

3. The method of claim 1 , further comprising determining a best reference frame based on the feature blocks.

4. The method of claim 1 , further comprising:

determining jitter for each frame based on the reference feature block map and the feature block map of the frame; and

adjusting each frame based on the determined jitter.

5. The method of claim 1 , wherein detecting at least one target comprises:

performing motion detection for the feature block map for each frame to obtain a first motion mask representing a string motion area and a second motion mask representing a weak motion area;

performing feature block region segmentation;

detecting high confident foreground feature blocks and high confident background feature blocks based on the motion detection;

performing AGC detection and compensation based on the high confident background feature blocks;

performing feature block change detection to obtain a first change mask representing a string change area and a second change mask representing a weak change area; and

detecting the target using the first and a second motion masks and the first and second change masks.

6. The method of claim 5 , wherein performing AGC detection and compensation based on the high confident background feature blocks comprises:

determining an intensity difference graph;

determining if there is any AGC effect for the frame;

if so, generating a look up table for different intensity levels; and

performing AGC compensation for the background model.

7. The method of claim 1 , wherein the features of the feature blocks are selected from the group consisting of: pixel intensity mean, pixel intensity gradient, low intensity centroid, high intensity centroid, low intensity pixel count, high intensity pixel count, and feature block label.

8. A computer-implemented method for video content analysis of a video comprising:

modeling a background of the video, comprising:

dividing each frame of the video into image blocks;

determining features for each image block of each frame to obtain feature blocks for each frame;

determining a feature block map for each frame based on the feature blocks of each frame;

determining a reference feature block map based on the feature blocks of frames of the video;

determining jitter for each frame based on the reference feature block map and the feature block map of the frame; and

adjusting each frame based on the determined jitter;

detecting at least one target in a foreground of the video based on the feature blocks of the video; and

tracking each target of the video.

9. The method of claim 8 , wherein determining jitter comprises:

selecting a reference feature block maps;

selecting a weight for feature blocks in the reference feature block map;

estimating coarse camera jitter; and

and estimating fine camera jitter.

10. The method of claim 9 , wherein estimating coarse camera jitter comprising:

selecting horizontal and vertical strong texture feature blocks from the reference feature block map;

generating a horizontal and vertical shift graph using the horizontal strong texture feature blocks and the vertical strong texture feature blocks; and

estimating coarse horizontal frame shifts and coarse vertical frame shifts.

11. The method of claim 10 , wherein estimating fine camera jitter comprises:

estimating local iterative camera jitter to obtain an error surface;

searching the error surface analysis to locate a minimum of a surface; and

determining a jitter estimation based on the minimum of the surface.

12. The method of claim 8 , wherein determining features for each image block of each frame to obtain feature blocks for each frame comprises:

determining a pixel intensity for each image block;

placing pixels in each image block into a category based on the pixel intensity;

determining a pixel intensity gradient for each image block;

determining pixels in the image block having a high intensity or a low intensity;

counting a number of pixels determined to have the high intensity or low intensity; and

determining the feature based on the pixel intensity gradient and the counting.

13. The method of claim 8 , further comprising determining a best reference frame based on the feature blocks.

14. The method of claim 8 , wherein detecting at least one target comprises:

performing motion detection for the feature block map for each frame to obtain a first motion mask representing a string motion area and a second motion mask representing a weak motion area;

performing feature block region segmentation;

detecting high confident foreground feature blocks and high confident background feature blocks based on the motion detection;

performing AGC detection and compensation based on the high confident background feature blocks;

performing feature block change detection to obtain a first change mask representing a string change area and a second change mask representing a weak change area;

detecting the target using the first and a second motion masks and the first and second change masks.

15. The method of claim 14 , wherein performing AGC detection and compensation based on the high confident background feature blocks comprises:

determining an intensity difference graph;

determining if there is any AGC effect for the frame;

if so, generating a look up table for different intensity levels; and

performing AGC compensation for the background model.

16. A computer-implemented method for video content analysis of a video comprising:

determining feature blocks for a video, comprising:

dividing each frame of the video into image blocks;

determining features for each image block of each frame to obtain feature blocks for each frame; and

determining a feature block map for each frame based on the feature blocks of each frame;

determining a background model of the video based on the feature block maps;

detecting at least one target in a foreground of the video based on the feature blocks of the video, comprising

identifying feature blocks of each frame that are likely background in the video as high-confident background feature blocks;

determining whether each frame has a video camera automatic gain control effect based on the high-confident background feature blocks; and

compensating for the video camera automatic gain control effect in each frame by adjusting the background model based on the high-confident background feature blocks; and

tracking each target of the video.