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M. Hofmann Prof. Dr. D. M. Gavrila

Looking at People - Detecting People in Images by their Body Parts. M. Hofmann Prof. Dr. D. M. Gavrila. Intelligent Systems Laboratory Informatics Institute, Faculty of Science University of Amsterdam Web: www.gavrila.net. Motivation for People Detection. pedestrian protection.

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M. Hofmann Prof. Dr. D. M. Gavrila

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  1. Looking at People - Detecting People in Images by their Body Parts M. HofmannProf. Dr. D. M. Gavrila Intelligent Systems LaboratoryInformatics Institute, Faculty of ScienceUniversity of AmsterdamWeb: www.gavrila.net

  2. Motivation for People Detection pedestrian protection surveillance (i.e. CASSANDRA system, see afternoon presentation) motion capture foranimation and games smart homes, elderly care robotic pets motion analysis (sports, medical)

  3. Project (Sub)Tasks • Detect people in images by • identifying regions of interest (ROIs) • detecting individual body parts (faces, head-shoulders, upper bodies, lower bodies) • combining results of individual body part-detectors • (This also is possible work-breakdown of 3 person DOAS team)

  4. 1. Identifying ROIs: Background Modeling Source: P. Withagen (UvA) Pixel-based methods Challenges • adjacent frame difference • mean & threshold • mean & covariance (single Gaussian) • mixture of Gaussians • Kalman filtering • „Time of Day“: gradual illumination changes • „Waving trees“: background can vacillate • „Shadows“ • „Camouflage“ • „Initialisation“

  5. 2. Detecting Individual Body Parts • Use of machine learning techniques • Viola & Jones approach (ICCV’2003): use Haar wavelet features • with AdaBoost cascade classifier stage 1 classifier stage 2 classifier stage N accepted hypotheses(detections) hypotheses hypotheses hypotheses rejected hypotheses

  6. 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

  7. Various Software System development under MS Visual Studio C++ environment.Use of following libraries / utilities: • Intel OpenCV Library, 2007http://www.intel.com/technology/computing/opencv/index.htmfor image filtering, individual body-part detectors, etc. • LibSVM, a library for Support Vector Machine classificationhttp://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)

  8. 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.

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