Object Classification - overview
Figure 1. Left: Result of classification system on a frame from video data provided by the IEEE Workshop on the Performance of Tracking in Surveillance 2001. Right: (left to right) The mask output from the background subtraction, the ellipse fitting and contour and the recurrent motion image used in the object classification for the person (top) and the car (bottom). Notice how the lower third of the person recurrent motion indicates the leg motion due to walking.
We are working to incorporate three significant algorithmic enhancements by including the following information: (i) probabilistic information regarding the likelihood that a pixel is associated with a real moving object or noise, (ii) the likelihood that the pixel is occluded and in which depth layer; (iii) reliable segment of object groups, object shadows; (iv) explicit motion (e.g., walking, driving) pattern detection; and (v) inferring true scale of objects and their motions from calibration information. We believe such a system will supply important quantitative bounds to object classification and allow us to achieve higher accuracy for a wide range of circumstances while maintaining real-time performance.
Click on the following image to see a demo (video 4MB MPEG1). “P” = person, “C” = car, "M" = multiple people, numerical values on the image represent the image velocity.
- Robust Background Subtraction
- Salient Motion Detection
- 2D Tracking
- 3D Multi-Person Tracking
- Articulated Human Body Tracking
- Active Head Tracking
- Coarse Head Pose Estimation
- Position Independent Absolute Head Pose Estimation
- Face Cataloger
- Video Privacy
- Multi-scale Tracking & Index Browser
- Real Time Alerts
- Middleware for Large Scale Surveillance (MILS)
- Performance Evaluation of Surveillance Systems
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