Optical flow analysis and Kalman Filter tracking in video surveillance algorithms
A SIMULINK-based algorithm for monitoring contacts in a surveillance video sequence using Optical Flow Analysis and Kalman Filters was developed. The Horn-Schunk Optical Flow Algorithm was used to identify contacts in a surveillance video sequence. The position and behavior of these contacts was...
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
Monterey California. Naval Postgraduate School
2012
|
| Online Access: | http://hdl.handle.net/10945/3521 |
| Summary: | A SIMULINK-based algorithm for monitoring contacts in a surveillance video sequence using Optical Flow Analysis and Kalman Filters was developed. The Horn-Schunk Optical Flow Algorithm was used to identify contacts in a surveillance video sequence. The position and behavior of these contacts was monitored by a modification of the traditional Kalman Filter. The Kalman Filter algorithm implemented has the ability to track up to ten contacts at a time, correctly assigning each of a maximum ten filters to their respective contacts on a frame-by-frame basis. Initial tests using artificial data show good performance of both the Optical Flow Analysis algorithm and the Kalman Filter Tracking algorithm. Surveillance video data was also used to test the algorithm with promising results. |
|---|