u niversiteit van a msterdam ias intelligent autonomous systems 1 m. hofmann prof. dr. d. m. gavrila...

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1 UNIVERSITEIT VAN AMSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute, Faculty of Science University of Amsterdam Web: www.gavrila.net Looking at People - Detecting People in Images by their Body Parts

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Page 1: U NIVERSITEIT VAN A MSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS 1 M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute,

1

UNIVERSITEITVAN

AMSTERDAM

IASINTELLIGENTAUTONOMOUSSYSTEMS

M. HofmannProf. Dr. D. M. Gavrila

Intelligent Systems LaboratoryInformatics Institute, Faculty of ScienceUniversity of AmsterdamWeb: www.gavrila.net

Looking at People - Detecting People in Images by their Body Parts

Page 2: U NIVERSITEIT VAN A MSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS 1 M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute,

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UNIVERSITEITVAN

AMSTERDAM

IASINTELLIGENTAUTONOMOUSSYSTEMS

motion capture foranimation and games

surveillance (i.e. CASSANDRA system,see afternoon presentation)

roboticpets

motion analysis (sports, medical)

pedestrian protection

smart homes, elderly care

Motivation for People Detection

Page 3: U NIVERSITEIT VAN A MSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS 1 M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute,

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UNIVERSITEITVAN

AMSTERDAM

IASINTELLIGENTAUTONOMOUSSYSTEMS

Project (Sub)Tasks

Detect people in images by

1. identifying regions of interest (ROIs)

2. detecting individual body parts (faces, head-shoulders, upper bodies, lower bodies)

3. combining results of individual body part-detectors

(This also is possible work-breakdown of 3 person DOAS team)

Page 4: U NIVERSITEIT VAN A MSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS 1 M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute,

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UNIVERSITEITVAN

AMSTERDAM

IASINTELLIGENTAUTONOMOUSSYSTEMS

1. Identifying ROIs: Background Modeling

Source: P. Withagen (UvA)

• adjacent frame difference• mean & threshold• mean & covariance

(single Gaussian)• mixture of Gaussians• Kalman filtering

Pixel-based methods

• „Time of Day“: gradual illumination changes• „Waving trees“: background can vacillate • „Shadows“• „Camouflage“• „Initialisation“

Challenges

Page 5: U NIVERSITEIT VAN A MSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS 1 M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute,

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UNIVERSITEITVAN

AMSTERDAM

IASINTELLIGENTAUTONOMOUSSYSTEMS

2. Detecting Individual Body Parts

• Use of machine learning techniques

• Viola & Jones approach (ICCV’2003): use Haar wavelet features

with AdaBoost cascade

hypotheses

classifier stag

e 2

classifier stag

e 1

classifier stag

e N

accepted hypotheses(detections)

hypotheseshypotheses

rejected hypotheses

Page 6: U NIVERSITEIT VAN A MSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS 1 M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute,

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UNIVERSITEITVAN

AMSTERDAM

IASINTELLIGENTAUTONOMOUSSYSTEMS

3. Combine Results of Individual Part-Detectors

• [Mohan2001, Wu2005]: fixed spatial layout, combination of contribution of individual part-detectors by weighted sum or by additional classifier

• [Mikolajczyk2004, Micilotta2005]: spatial distribution is learnt, estimation of joint probabilities

Page 7: U NIVERSITEIT VAN A MSTERDAM IAS INTELLIGENT AUTONOMOUS SYSTEMS 1 M. Hofmann Prof. Dr. D. M. Gavrila Intelligent Systems Laboratory Informatics Institute,

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UNIVERSITEITVAN

AMSTERDAM

IASINTELLIGENTAUTONOMOUSSYSTEMS

Various

• Intel OpenCV Library, 2007 http://www.intel.com/technology/computing/opencv/index.htm for image filtering, individual body-part detectors, etc.

• LibSVM, a library for Support Vector Machine classification http://www.csie.ntu.edu.tw/~cjlin/libsvm/

• Daimler Image Label Tool, ROC utilities

Dataset

• Training: already pre-trained V&J cascade detectors: OpenCV, UvA any others from the web?

• Test: CASSANDRA dataset (about 5000 images, partially labeled, consider only fully visible people)

System development under MS Visual Studio C++ environment.Use of following libraries / utilities:

Software

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IASINTELLIGENTAUTONOMOUSSYSTEMS

Bibliography

• [Gavrila1999] D. M. Gavrila. „The Visual Analysis of Human Movement: A Survey“, Computer Vision and Image Understanding, 73(1):82-98, 1999

• [DOAS2007] S. Korzec, H. Visser and M. Goksun. “Detecting Humans by Combining Human Part-detectors in an Urban Setting”. DOAS Final Project 2007.

• [Viola2003] P. Viola, M.J. Jones and D. Snow. „Detecting Pedestrians using Patterns of Motion and Appearance“. Proc. of ICCV, pp.734-741, Nice, France, 2003.

• [Mohan2001] A. Mohan, C. Papageorgiou and T. Poggio „Example-Based Object Detection in Images by Components“, IEEE Transactions on PAMI, 23 (4), pp. 349-361, 2001.

• [Micilotta2005] A.S. Micilotta, E.J. Ong and R. Bowden. “Detection and Tracking of Humans by Probabilistic Body Part Assembly”. BMVC’05.

• [Wu2005a] B. Wu and R. Nevatia. “Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors”, ICCV’05.

• [Mikolajczyk2004] K. Mikolajczyk, D. Schmid, A. Zisserman, “Human detection based on a probabilistic assembly of robust part detectors”, Proc. ECCV, Prague, Czech Republic, May 11–14, 2004.