This survey and taxonomy of location systems for mobile-computing applications describes...

Visual analysis of human motion is currently one of the most active research topics in computer vision. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Human motion analysis concerns the detection, tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. This paper provides a comprehensive survey of research on computer vision based human motion analysis. The emphasis is on three major issues involved in a general human motion analysis system, namely human detection, tracking and activity understanding. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are discussed.

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We present a system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized cameras located far from each other. The system improves upon existing systems in many ways including: (1) We do not assume that a foreground connected component belongs to only one object; rather, we segment the views taking into account color models for the objects and the background. This helps us to not only separate foreground regions belonging to different objects, but to also obtain better background regions than traditional background subtraction methods (as it uses foreground color models in the algorithm). (2) It is fully automatic and does not require any manual input or initializations of any kind. (3) Instead of taking decisions about object detection and tracking from a single view or camera pair, we collect evidences from each pair and combine the evidence to obtain a decision in the end. This helps us to obtain much better detection and tracking as opposed to traditional systems.

This paper presents an overview of the issues and algorithms involved in creating this semiautonomous, multicamera surveillance system

A computer vision system for tracking multiple people in relatively unconstrained environments is described. Tracking is performed at three levels of abstraction: regions, people and groups. A novel, adaptive background subtraction method that combines color and gradient information is used to cope with shadows and unreliable color cues. People are tracked through mutual occlusions as they form groups and separate from one another. Strong use is made of color information to disambiguate occlusions and to provide qualitative estimates of depth ordering and position during occlusion. Simple interactions with objects can also be detected. The system is tested using both indoor and outdoor sequences. It is robust and should provide a useful mechanism for bootstrapping and reinitialization of tracking using more specific but less robust human models. Key Words: background subtraction, groups of people, human activity, tracking 1.

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.

This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this framework, the background is represented by the most significant and frequent features, i.e., the principal features, at each pixel. A Bayes decision rule is derived for background and foreground classification based on the statistics of principal features. Principal feature representation for both the static and dynamic background pixels is investigated. A novel learning method is proposed to adapt to both gradual and sudden "once-off" background changes. The convergence of the learning process is analyzed and a formula to select a proper learning rate is derived. Under the proposed framework, a novel algorithm for detecting foreground objects from complex environments is then established. It consists of change detection, change classification, foreground segmentation, and background maintenance. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks. Good results of foreground detection were obtained. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results.

Emerging mobile computing applications often need to know where things are physically located. To meet this need, many dierent location systems and technologies have been developed. In this paper we present a the basic techniques used for location-sensing, describe a taxonomy of location system properties, present a survey of research and commercial location systems that de ne the eld, show how the taxonomy can be used to evaluate location-sensing systems, and oer suggestions for future research. It is our hope that this paper is a useful reference for researchers and location-aware application builders alike for understanding and evaluating the many options in this domain.

Pedestrian detection is essential to avoid dangerous traffic situations. In this paper, we present a fast and robust algorithm for detecting pedestrians in a cluttered scene from a pair of moving cameras. This is achieved through stereo-based segmentation and neural network-based recognition. The algorithm includes three steps. First, we segment the image into sub-image object candidates using disparities discontinuity. Second, we merge and split the sub-image object candidates into sub-images that satisfy pedestrian size and shape constrains. Third, we use intensity gradients of the candidate sub-images as input to a trained neural network for pedestrian recognition. The experiments on a large number of urban street scenes demonstrate that the proposed algorithm: 1) can detect pedestrians in various poses, shapes, sizes, clothing, and occlusion status; 2) runs in real-time; and 3) is robust to illumination and background changes.