Detecting Abandoned Objects With a Moving Camera 指導教授:張元翔 老師...
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Transcript of Detecting Abandoned Objects With a Moving Camera 指導教授:張元翔 老師...
Detecting Abandoned Objects With a Moving Camera
指導教授:張元翔 老師學生:資訊碩一 9977003 吳思穎
Outline
• Introduction........................................3
• Related work......................................4
• Proposed approach.............................6
a. Intersequence Geometric Alignment........8
b. Intrasequence Geometric Alignment.......10c. Temporal Filtering............................13
• Experimental results.........................14
• Conclusion........................................17
Introduction
• Dynamic suspicious behaviors
• Static dangerous objects
Related work• 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.
Proposed approach
• Intersequence Geometric Alignment
• Intrasequence Geometric Alignment
• Temporal Filtering
Intersequence Geometric Alignment
• SIFT feature descriptor (128-dimensional)
• RANSAC v.s mRANSAC
Fig. 3. Examples of alignment based upon RANSAC and modified RANSAC (mRANSAC).
First column: the reference and target frames.Second column: best inliers obtained from RANSAC and mRANSAC respectively. Third column: the aligned reference frames based upon RANSAC and mRANSAC.Fourth column: difference images between the aligned reference frames.
Intrasequence Geometric Alignment
• Removal of False Alarms on High Objects
• Removal of False Alarms on the Dominant Plane
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• Fig. 6. Illustration of intrasequence alignment for RR and removing false alarms on high objects based upon the alignment results. See text in Section III-B-I for details
• Fig. 8. Illustration of intrasequence alignment for both TT and RR, and removing false alarms in flat areas by local appearance comparison of the alignment results.
Temporal Filtering
Goal :
To confirm the existence of suspicious objects.
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.
Thank you everybody~