Pedestrian Attribute Analysis Using a Top-View Camera in a Public Space

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.