Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Human Carrying Status in Visual Surveillance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ACM Computing Surveys (CSUR)
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
HMM based action recognition with projection histogram features
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Estimating human body and head orientation change to detect visual attention direction
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper, we propose a method to analyze gender of the pedestrian and whether he or she has a baggage or not in a public space. The challenging part of this work is we only use top-view camera images to protect the pedestrians' privacy. We focused on temporal changes in their position, shape, and contours over the frames because their appearances do not provide much information. We extracted the pedestrians' features using their position, area, aspect ratio, histogram of oriented gradients (HoG), and Fourier descriptors. The temporal information was taken into consideration by employing Gaussian mixture models (GMM), GMM universal background model (GMM-UBM), and bag of features (BoF) model. The attributes were classified by using support vector machines (SVM). We conducted experiments using 60-minute video captured by a top-view camera attached at an airport. Experimental results show that the classification accuracy is 69% for the gender classification and 79% for baggage possession classification.