Tracking and Surveillance Projects
I took a special topics course in Spring 2008 at Georgia Tech, ECE 8893: Embedded Video Surveillance Systems. The course included three projects, each shown below. Detailed information about the algorithm is in the source code comments. (All the source is in Python)
Project 1: Activity Density Estimation
Use background subtraction to find moving foreground objects in a video sequence. Then, color-code regions with the most activity. Here is the result:
Source: p1.py
Project 2: Styrofoam Airplane Tracking
Find all white styrofoam planes in the scene and track them throughout the scene. We used color thresholding and simple dynamics to do the tracking.
Source: p2.py
Project 3: Pedestrian Tracking
Count and track the pedestrians that cross on a busy sidewalk. We use a combination of motion estimation via background subtraction and feature matching using the Bhattacharyya measure.
Most of this code is very hack-y because it was done quickly. However, it was
fun to learn Python, and the class was enjoyable overall.
Some very cool stuff going on here! I can imagine tons of real life uses for this.
So is this actual tracking or just detections? Do you initiate tracks and then frame to frame correlate observations to determine if one detection in one frame corresponds to a detection in another frame?
The results for projects 2 and 3 perform detection *and* tracking. Objects are detected in each frame and then the detected objects are correlated from previous frames using image data and kinematics to maintain track.
There are also detailed explanations of the algorithms in the comments of each file.