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My Perspectives on Graduate Research
Panya ChanawangsaUbiquitous Multimedia Lab
Advisor: Dr. Chang Wen Chen
10/14/2014
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About Myself
• SUNY Buffalo, Ubiquitous Multimedia Lab5th year PhD student
• Xerox CorporationRochester, New York
August 2012 – May 2013
• AFT Computer VisionSeattle, Washington
June 2013 – August 2013
• AFT Computer Vision: Surveillance Camera Applications GroupSeattle, Washington
May 2014 – August 2014
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Ubiquitous Multimedia Lab
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Ubiquitous
Multimedia Lab
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Ubiquitous Multimedia Lab
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Agenda
• Overview of my group’s research area• Overview of my research area• My PhD research• Exciting (and not so exciting) aspects of doing research• What I wish I had known when I joined the program• Q&A
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Ubiquitous Multimedia Lab
• HTTP live streaming• Video transmission over various networks• Mobile video adaptation• Quality of experience for multimedia consumers• Multimedia in social media context• Computer vision and image processing
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Ubiquitous Multimedia Lab
• HTTP live streaming• Video transmission over various networks• Mobile video adaptation• Quality of experience for multimedia consumers• Multimedia in social media context• Computer vision and image processing
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My Research Overview
Computer Vision for Intelligent Transportation Systems
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Input image
Computer Vision System
Useful information
Puppy, 0.94
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Wikipedia
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Computer Vision and its Applications
Face recognition
Amazon Fire Phone face tracking
Facebook facial detection/recognition
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Computer Vision and its Applications
Image search, Image retrieval
Google Image Amazon Firefly
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Computer Vision and its Applications
Beauty recommendation systems
Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba, Wow! You are so beautiful today!, ACM International Conference on Multimedia, pp. .
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Beauty recommendation systems
Recommendation System
Synthesized result
Recommendation results
“You should do the following:- Have long hair with curls.- Use black eye shadow.- Use number 3 foundation.”
Input image
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Why Computer Vision is Hard
Is there a human in the image?
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Why Computer Vision is Hard
Input image Features Classifier
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“The new approach gives near-perfect separation on the original MITpedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.”
Naveet Dalal and Bill Triggs, Histogram of Oriented Gradients for Human Detection, CVPR 2005.
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Why Computer Vision is Hard
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Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba, HOGgles: Visualizing Object Detection Features, IEEE International Conference on Computer Vision. 2013.
Why Computer Vision is Hard
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Intelligent Transportation Systems
Red light cameras High-occupancy vehicle lane License plate number recognition
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Intelligent Transportation Systems
Smart parkingReal-time traffic monitoring
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My Research Overview
• Lane departure warning system• Overtaking vehicle detection• Smart parking• Drunk-driving detection
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Lane Departure Warning System
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Research and Implementation Challenges
• Feature selection: color? edge?• Feature detection: • Resource constraint: energy, processing power• Efficiency: can we meet the real-time requirement?• Implementation: Android? iOS?• Result validation: ground-truth generation
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Overtaking Vehicle Detection System
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Research and Implementation Challenges
• Feature selection: HOG? Symmetry?• Feature detection: highly dynamic scene• Efficiency: can we meet the real-time requirement?• Accuracy: how do we make an accurate prediction
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Drunk Driving Detection
Is this driver drunk?
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Basic Idea
1. Use NHTSA’s visual cues for police officers.
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Basic Idea
2. What are some of the effects of alcohol on driving performances? User studies: in collaboration with Dr. Sean Wu from the IE department
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Basic Idea
3. Approach the problem from ground up.
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Driving Parameters
•Ability to maintain lateral positions•Speed variability•Stopping distance from the stop signs and traffic lights•Turning radius
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Data Acquisition
BumblebeeXB 3
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Initial System Setup
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3D camera
IEEE 1394 cables
Jib
Weights
Safety triangle
Portable battery
Laptop
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Dataset
Tracking of instrument vehicle
Multiple vehicle tracking
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Dataset
Lane keeping
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Dataset
Turning radius
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Dataset
Stopping distance
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3D Processing
Vehicle mask Vehicle point cloud
front view top view
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Extracted 2D/3D Trajectories
50 100 150 200 250 300
50
100
150
200
Trajectories of all the vehicles in data set 1 1.5
2
2.5
3
3.5
-20-15
-10-5
00
20
40
60
80
X (m)
3D Trajectory of the Vehicle
Y (m)
Z (
m)
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What I wish I had known way back
• Have many interests; focus on one.• Four years is a short period of time.• Treat your PhD like a full-time job.• Prioritize your tasks.• Make sure you are truly passionate about your research topic.• Ask yourself what you really want to do in life.• Do internships.
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What gets me excited
• Freedom to pursue my academic curiosity• Collaboration with top-notch researchers on funded projects• High-impact and practical research• Computer vision applications are everywhere.• Lots of research challenges and extremely difficult problems:
Object recognition Action recognition Robotics
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Academic vs. Industry Research
• Access to large datasets• Shared codebase vs. implementing everything yourself• Freedom to pursue your research interests• Funding