Professor Zhigang Zhu
Department of Computer Science
City College of New
York and Graduate Center
The City University of New York (CUNY)
Time: Tuesday 11:45 am -1:45 pm
Room: 3305
CRN Code: 10444
Credits: 3.0
Office Hours: Tuesday
10:00
-
11:30
am,
Rm 4439
The course will discuss the
state-of-the-art of target (e.g., human, vehicle) detection,
classification and
tracking using vision and multimodal sensing.
We will review the major algorithms and methods for human and
other
target detection, classification and tracking from the most recent
conference
and journal papers (CVPR, ICCV, PAMI, IJCV, etc). The course will
include
several lectures by the instructor on the fundamentals, a few readings
and
presentations by the students, and a final project by each student.
1.
Detection: Hypothesis Generation
1.0. Stationary
Cameras or
1.1. Brute Force
Approach:
Sliding Window Technique
1.2. Motion:
Background
Subtraction or Optical Flow
1.3.
Appearance/Color:
Interest Point Detectors
1.4. Stereo: 3D Cues
2.
Classification: Model Matching
2.1. Generative
Models – a
Bayesian Approach
A.
Shape,
Texture
and 3D Cues
B.
Exampar-based
models:
distance transformation
C.
GMMs
and
EM-based approaches
D.
Combined
Shape
and Texture Models
2.2. Discriminative
Models- a
Classification Approach
A.
Features
(Wavelet,
Codebook, HOG, Salient
structures, Spatio-temporal features)
B.
Classifiers
(a.
SVM b. AdaBoost c. ANN …)
2.3. Integration of
Generative and Discriminative Models
A.
A
Mixed
Generative-Discriminative Framework
B.
Pictoral
Structures
Approach
C.
Hybrid
Body
Representation
3.
Tracking: Temporal
Association
3.1. Kalman
Filtering
3.2. Particle
Filtering
3.3. Integration of
Classification
and Tracking
4. Use
of 3D, Motion and Multiple Cues
4.2. Ground Plane Assumption
4.4. 3D in Matching and Tracking
4.5. More on Multiple Cues
Monocular model-based
3D
tracking
of rigid objects. V.
Lepetit and P. Fua, Source,
Foundations and Trends® in Computer Graphics and Vision, 2005
Monocular
Pedestrian
Detection:
Survey
and
Experiments. M. Enzweiler and D.
M. Gavrila. IEEE Transactions on Pattern
Analysis and Machine Intelligence (PAMI), available online: IEEE
Computer
Society Digital Library, http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.260,
17
Oct.
2008.
Multi-cue
Pedestrian
Detection
and
Tracking
from a Moving Vehicle, Gavrila D., Munder S., IJCV(73), No. 1,
June 2007,
pp. 41-59.
Pedestrian Detection: A Benchmark, Piotr Dollár, Christian Wojek, Bernt Schiele, Pietro Perona, CVPR 09
Towards Practical Evaluation of Pedestrian
Detectors, Mohamed Hussein, Fatih Porikli, Larry Davis,
TR2008-088, MITSUBISHI ELECTRIC RESEARCH LABORATORIES April 2009
Results
from
a
Real-time
Stereo-based
Pedestrian Detection System on a Moving Vehicle,
Max
Bajracharya,
Baback Moghaddam, Andrew Howard, Shane Brennan, Larry H.
Matthies
Real-Time
Human
Detection
in
Uncontrolled
Camera Motion Environments. Mohamed Hussein. Wael
Abd-Almageed. Yang Ran.
Larry Davis, ICVS 2006
Hierarchical
Part-Template Matching for Human
Detection and Segmentation. by: Zhe Lin and Larry S. Davis and
David S.
Doermann and Daniel DeMenthon, ICCV 2007
On page(s): 380-391, Volume: 10, Issue: 3,
Sept. 2009
Pedestrian Detection for Driving
Assistance Systems:
Single-frame
Classification and System Level
Performance.
Amnon Shashua. Yoram Gdalyahu. Gaby Hayun, IV 2004
Monocular
Pedestrian
Recognition
Using
Motion
Parallax, M. Enzweiler1, P. Kanter2
and D. M.
Gavrila23, IV 2008
Dynamic 3D
Scene Analysis
from a Moving Vehicle, B.
Leibe,
N. Cornelis,
K. Cornelis,
and L.
Van Gool. in
IEEE Conference on Computer Vision and Pattern Recognition (CVPR'07 Best Paper
Award)
Background Subtraction
for Freely Moving Cameras (PDF),
Yaser Sheikh, Omar Javed,
Takeo
Kanade. ICCV09
Histograms
of Oriented Gradients for Human Detection.
Navneet Dalal and Bill Triggs, CVPR 2005
Human
Detection Using Oriented Histograms of Flow
and Appearance, Navneet Dalal, Bill Triggs, and Cordelia Schmid,
ECCV 2006
Fast
Human
Detection
in
Crowded
Scenes by Contour Integration and Local Shape Estimation,
Csaba Beleznai, Horst Bischof, CVPR 09
Multi-Cue
Onboard
Pedestrian
Detection, Christian Wojek, Stefan Walk, Bernt
Schiele, CVPR 09
Human
Detection Using Partial Least Squares Analysis.
William Robson Schwartz, Aniruddha Kembhavi, David Harwood, Larry S.
Davis,
ICCV 2009
Hybrid
Body Representation for Integrated Pose
Recognition, Localization and Segmentation.
Cheng Chen and Guoliang Fan, CVPR 2008
Multi-sensor
Detection
and
Tracking
of Humans for
Safe Operations with Unmanned.
Ground Vehicles. Susan M. Thornton, Mike Hoffelder and Daniel
D. Morris, Workshop
on Human
Detection from
Correlated
Probabilistic Trajectories for Pedestrian
Motion Detection. Frank Perbet, Atsuto Maki, Björn
Stenger, ICCV 2009
Detection
Driven Adaptive Multi-cue Integration
for Multiple Human Tracking. Ming Yang, Fengjun Lv, Wei Xu,
Yihong Gong,
ICCV 2009
People-Tracking-by-Detection
and
People-Detection-by-Tracking, M. Andriluka,
S. Roth and
B. Schiele, CVPR 2008
Abnormal Crowd
Behavior Detection using Social Force Model, Ramin
Mehran,
Alexis
Oayama,
Mubarak Shah, CVPR 09
Copyright @ Zhigang
Zhu ( zhu at cs.ccny.cuny.edu
), Spring 2010.