Curtis Kelsey University of Missouri [email protected] A FINGERPRINTING SYSTEM MOBILE MODEL FOR...
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Transcript of Curtis Kelsey University of Missouri [email protected] A FINGERPRINTING SYSTEM MOBILE MODEL FOR...
An Analysis of Fingerprinting System Components for Video Copy Detection
Curtis Kelsey
University of Missouri
A Fingerprinting system mobile model for video copy protection
motivation
Create Application/Database ecosystems free of copyright infringement
Reduce computational cost incurred on the provider.
Proposed technique
Use a modified pairwise boosting on visual Viola-Jones features to learn top-M discriminative filters on a mobile platform for querying.
Characteristic analysis
Benefits
As accurate as the time spent training
Allows for poor false positive rate
Weaknesses
All classifiers must have a high detection rate
OpenCV harrtraining (implementation analysis)
Training the classifier requires:
Negative samples for training/testing
Positive samples for training/testing
Training Time
~90 minutes w/ 1350+ and 5500- images [5]
Classifier Accuracy
> 5000 false detections per 1.3 billion [5]
Naotoshi Seo extensively tests OpenCVs training [6]
As training time increases, accuracy increases in a logarithmic form
feasibility
Can we use cascading classifiers on a mobile device?
No
Why?
Video Data is unknown until submission. Classifier training cannot be done in real-time
What now
Use another fingerprinting technique for the mobile platform
Modified proposed technique
Use a modified block-based luminance signature generated by a client for submission to a server for copy detection.
Metrics
Precision
Recall
False Positive
False Negative
In a system attempting to filter copyrighted intellectual property, the false negative rate can be discarded, giving the benefit of the doubt to the user uploading video into your environment.
X
Ntp is the number to true positives/correct matches
Np is the total number of positives/matches
Nep is the number of expected positives/matches
Nen is the number of expected negatives
Nfp is the false positive rate
Nfn is the false negative rate
8
First things first
Eliminate Preprocessing
What was done?
Video size constrained
Frame rate constrained
Encoding bit rate constrained
Transition intensity
Calculate frame intensity
FI =
Calculate transition intensity
Determine the threshold between scenes
Determine the number of scenes
CONVERT RGB to YUV
Y` is a measure of overall luminance
Can be used instead of components
SCENE frames
Meng et al. describes multiple solutions. I use a basic luminance differencing in the temporal domain.
Threshold needs to be trained
Generate fingerprint
Use the scene frames to generate block luminance signatures of each frame
Base on ordinal ranking
Weak to affine transformations
S
//Represented with k
//Represented with m
//Represented with k
Submitting the fingerprint
POST fingerprint to php script via internet
Use Direct Hashing Algorithm (DHA) previously presented.
Hash fingerprints
Insert into a standard hash table if query returns no match
Query up to hamming distance of 2
Results
Frames process in approx. 12.5 seconds each
Core i7
4GB DDR3
Video Size
1676 x 985
Data Rate
159kbps
results
Like hardware
1280 x 720
15,513 kbps
29 fps
References
[1] Lian, H. C., Li, X. Q., & Song, B. (2011). A fingerprinting system for video copy detection. Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on (Vol. 4, pp. 21462149). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6019957
[2] Viola, P. (2001). Rapid object detection using a boosted cascade of simple features. , 2001. CVPR 2001. Proceedings of the. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=990517
[3] Zhang, Z., Cao, C., & Zhang, R. (2010). Video copy detection based on speeded up robust features and locality sensitive hashing. Automation and Logistics (, 13-18. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5585375
[4] Meng, J., Juan, Y., & Chang, S.-fu. (1995). Scene Change Detection in a MPEG Compressed Video Sequence 2 . Previous Approaches 3 . MPEG Compression Standard. Symposium A Quarterly Journal In Modern Foreign Literatures, 2419(February), 1-12. Retrieved from http://csce.uark.edu/~jgauch/library/Video-Segmentation/Meng.1995.pdf
[5] Adolf, Florian. How-to build a cascade of boosted classifiers based on Haar-like features. Retrieved from http://lab.cntl.kyutech.ac.jp/~kobalab/nishida/opencv/OpenCV_ObjectDetection_HowTo.pdf
References cont.
[6] Seo, Naotoshi. Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features). Retrieved from http://note.sonots.com/SciSoftware/haartraining.html
[7] Mohan, R. (1998). Video sequence matching. Acoustics, Speech and Signal Processing, 1998., 3697-3700. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=679686
Questions