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

Abstract

Abstract. 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.Keywords: Human attributes, surveillance, gender classification, bag possession classification. IntroductionVisual surveillance has been one of the most active research areas in computer vision. Surveillance cameras have been installed in a lot of places in such as stations, airports, or on the streets for security purposes. Visual surveillance data are easy to analyze for humans. On the other hand, analyzing the data by computers requires a wide range of algorithms such as moving object detection, object classification, counting, tracking, behavior labeling, human identification, abnormal object/event detection, flux analysis, data fusion collected from multiple cameras, and so on. Understanding human attribute and behavior, in particular, is getting more attention not only for security reasons but for better services, marketing, and so on. If surveillance systems can recognize gender and age range of the passengers, digital 542 T. Yamasaki and T. Matsunami singnage dedicatedly designed for a particular target can be displayed. If systems detect children who are alone, they might be lost and looking for their parents. In addition, systems can alert person who is carrying a large suitcases widely spread behind him/her, which is dangerous and is becoming a significant safety issue in crowded airports and stations. For activity recognition, Chen and Hauptmann proposed MoSIFT [3]. MoSIFT was an extension of the Scale Invariant Feature Transform (SIFT) [4] features to the temporal domain and showed its superiority to Histogram of Oriented Gradients (HoG) [5] . Zhang et al. analyzed the optimal camera angle for the gender classification using SMV classifiers [10], in which only yaw angles were considered. In these approaches, however, the quality of the images was well-controlled: target objects were large enough, taken from the frontal-view, and so on. On the other hand, only top-view images taken by a surveillance camera is used in this work, which could protect the pedestrians' privacy. Another challengin...

Human Attribute Analysis Using a Top-View Camera Based on Two-Stage Classification

“…The classification accuracies of our previous work [8] Table 3 Combinations for majority voting. were only 68.5% for the gender classification and 78.8% for the bag-possession status classification, respectively.…”

“…were only 68.5% for the gender classification and 78.8% for the bag-possession status classification, respectively. Note that [8] used exactly the same data as those in this paper. Therefore, it is clear that the present work can drastically improve the classification accuracy.…”

“…The pedestrian detection and tracking is based on [8]. A background image is generated for every frame by averaging the previous 60 frames.…”

“…For bag-possession classification, p/g/z-type Fourier descriptors [9] with GMM universal background model (GMM-UBM) [10] have been employed. The classification accuracies for the gender and the bagpossession status were 68.5% and 78.8%, respectively [8].…”

“…We have also been developing a human attribute analysis system to classify the gender and the bag-possession status [8]. The difference from previous works is that we only use top view cameras.…”