The resume of me. Please refer to it if interested.

1. RESUME
YC.WANG

2. EXPERIENCE
2006.9~
2010.6
2010.9~
2012.6
2013.2~
2015.2
NCTU Bachelor Degree of
Electrical and Computer Engineering
Communication system, Probability Theory,
Computer Network
NTU Master Degree of
Graduate Institute of Communication
Digital signal Processing, Queue theory, Image Processing
2011.6~
2012.6
Academia Sinica
Research Assistant
Anomaly Detection and Localization via
Foreground Motion Information
Work&Travel in
NJ USA&VAN CA
2012.6~
2012.10
Facility Eng at TSMC
Special gas System construct, maintain&monitoring
Test&Develop Eng at TSMC
Customer TV/IP Verify
Passive device Verify
2015.2~
Now
Name
王逸辰/YC WANG
Date of Birth
15 July 1988
Phone
E-mail
ianwang0715@gmail.com

3. HOBBIES&INTERESTS
• Swimming
๏Strength you body and mind
• Darts
๏Make you concentrate on your target
• Reading
๏Expand your eyesight of your life
• Talk Show
๏Let people read for you

4. • Introduction
• Related Work
• Proposed Method
• Experimental Results
• Conclusion
OUTLINE

5. • Anomalies are patterns in data that do not conform to a well
defined notion of normal behavior.
Normal_1
Normal_2
Anomaly_1
Anomaly_2
Anomaly_3
[1]V. Chandola, A Banerjee, and V Kumar, “Anomaly Detection: A Survey,” (CSUR), 2009.
DEFINITION

6. FRAMEWORK

7. • Post Image Processing
• Eliminate the artificial holes
• Erosion & Dilation
BACKGROUND SUBTRACTION

8. BLOB MOTION TENDENCY ESTIMATION(1/2)
' '
( , , 1)p x y t +
( , , )p x y t
( , , )vx x y t
( , , 1)p x y t +( , , 1)p x y t −
'' ''
( , , 1)p x y t −
( , , 1)vx x y t −
( , , 1)vx x y t −
( , , )vy x y t
t t+1t-1

9. BLOB MOTION TENDENCY ESTIMATION(1/2)
t t+1 t+2 t+3t-1t-2t-3

10. FEATURE EXTRACTION(1/2)
* Magnitude
Histogram
Filter out the
noise

11. FEATURE EXTRACTION(2/2)
* * * * **
Normalization

12. EXAMPLES OF BLOB

13. NONLINEAR DIMENSION REDUCTION(1/2)
1. K nearest neighbors
2.
( , )G
computing the shortest path
d i j in the graph G
2
3. ( ) ( )G Y L
E D Dτ τ= −
Traditional Linear PCA

14. NONLINEAR DIMENSION REDUCTION(2/2)
• One-class classification
• Positive instance (normal blobs) act similarly
• Negative ones (abnormal blobs) act differently in their own
ways

15. RESULTS EVALUATION
• ROC curve
– Generated by varying the threshold
– Want to minimize Equal Error Rate (EER)
True
Positive
Rate
False Positive Rate
Equal Error Rate
(ERR)
# False positives
# False Positives + True Negatives
# True positives
# True Positives + False Negatives
Ideal Case
False
Negative
Rate

16. RESULT - FRAME LEVEL
16
[8]A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz. “Robust real-time unusual event detection using multiple
fixed location monitors,” (PAMI), 2008
[9]J. Kim and K Grauman, “Observe Locally, Infer Globally: a Space-Time MRF for Detection Abnormal Activities
with Incremental Update,” (CVPR), 2009.

17. RESULT - PIXEL LEVEL

18. EXPERIMENT RESULTS
• Dataset: UCSD Anomaly Detection Dataset
Ped1: Train (32) & Test(34) Ped2: Train (16) & Test (12)
Our own film

19. WORK&TRAVEL
Skydiving Yankee Stadium
Wall Street
Baltimore Orioles
Walk of Fames
White House

20. • Customer’s Test Vehicle Verify
1. Familiar with multiple platform to accomplish various of
tasks
2. Develop tools for
data analysis
• Customer’s IP Verify
1. Around-the-clock Support
for the Customer’s NTO
• IPD Function Verify
1. WLFT
2. FT
TEST&DEVELOP

21. Certificate of Degree of NTU

22. Certificate of Degree of NCTU

23. Internship of Academia Sinica