23-08-2012, 04:26 PM
Detecting Abandoned Objects With a Moving Camera
[attachment=32549]
Spengler and Schiele：
a tracking/surveillance system to automatically detect abandoned objects and draw the operator’s attention to such events
Porikli et al.：
use two foreground and two background models for abandoned object detection
Guler and Farrow：
use a background-subtraction based tracker and mark the projection of the center of mass of each object on the ground
Smith et al.：
use a two-tiered approach
Related work
All of the previously mentioned techniques utilize the static cameras installed in some public places, where the background is stationary.
However, for some application scenarios, the space to keep a watch on is too large to use static cameras.
Therefore, it is necessary to use a moving camera to scan these places.
Experimental results
The length of the 15 test videos ranges from 70 to 230 frames.
The height of our test objects ranges from about 4–80 cm.
There are about 23 suspicious objects overall and we successfully detect 21 of them.
Detection fails in only one video where the test object is almost flat.
Conclusion
Our algorithm finds these objects in the target video by matching it with a reference video that does not contain them.
[attachment=32549]
Spengler and Schiele：
a tracking/surveillance system to automatically detect abandoned objects and draw the operator’s attention to such events
Porikli et al.：
use two foreground and two background models for abandoned object detection
Guler and Farrow：
use a background-subtraction based tracker and mark the projection of the center of mass of each object on the ground
Smith et al.：
use a two-tiered approach
Related work
All of the previously mentioned techniques utilize the static cameras installed in some public places, where the background is stationary.
However, for some application scenarios, the space to keep a watch on is too large to use static cameras.
Therefore, it is necessary to use a moving camera to scan these places.
Experimental results
The length of the 15 test videos ranges from 70 to 230 frames.
The height of our test objects ranges from about 4–80 cm.
There are about 23 suspicious objects overall and we successfully detect 21 of them.
Detection fails in only one video where the test object is almost flat.
Conclusion
Our algorithm finds these objects in the target video by matching it with a reference video that does not contain them.