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232
Learning Patterns of Activity Using Real-Time Tracking
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activit ..."
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Cited by 898 (10 self)
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Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion
Image Change Detection Algorithms: A Systematic Survey
- IEEE Transactions on Image Processing
, 2005
"... Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. T ..."
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Cited by 236 (3 self)
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Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.
Cyclops: In situ image sensing and interpretation in wireless sensor networks
- In SenSys
, 2005
"... Despite their increasing sophistication, wireless sensor networks still do not exploit the most powerful of the human senses: vision. Indeed, vision provides humans with unmatched capabilities to distinguish objects and identify their importance. Our work seeks to provide sensor networks with simila ..."
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Cited by 133 (6 self)
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Despite their increasing sophistication, wireless sensor networks still do not exploit the most powerful of the human senses: vision. Indeed, vision provides humans with unmatched capabilities to distinguish objects and identify their importance. Our work seeks to provide sensor networks with similar capabilities by exploiting emerging, cheap, lowpower and small form factor CMOS imaging technology. In fact, we can go beyond the stereo capabilities of human vision, and exploit the large scale of sensor networks to provide multiple, widely different perspectives of the physical phenomena. To this end, we have developed a small camera device called Cyclops that bridges the gap between the computationally constrained wireless sensor nodes such as Motes, and CMOS imagers which, while low power and inexpensive, are nevertheless designed to mate with resource-rich hosts. Cyclops enables development of new class of vision applications that span across wireless sensor network. We describe our hardware and software architecture, its temporal and power characteristics and present some representative applications.
A system for learning statistical motion patterns
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of th ..."
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Cited by 119 (1 self)
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permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. © 2006 IEEE. Copyright and all rights therein are retained by authors or by other copyright holders. All persons downloading this information are expected to adhere to the terms and constraints invoked by copyright. This document or any part thereof may not be reposted without the explicit permission of the copyright holder. Citation for this copy:
Counting people in crowds with a real-time network of image sensors
- in Proc. of IEEE ICCV
, 2003
"... Estimating the number of people in a crowded environment is a central task in civilian surveillance. Most vision-based counting techniques depend on detecting individuals in order to count, an unrealistic proposition in crowded settings. We propose an alternative approach that directly estimates the ..."
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Cited by 81 (2 self)
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Estimating the number of people in a crowded environment is a central task in civilian surveillance. Most vision-based counting techniques depend on detecting individuals in order to count, an unrealistic proposition in crowded settings. We propose an alternative approach that directly estimates the number of people. In our system, groups of image sensors segment foreground objects from the background, aggregate the resulting silhouettes over a network, and compute a planar projection of the scene’s visual hull. We introduce a geometric algorithm that calculates bounds on the number of persons in each region of the projection, after phantom regions have been eliminated. The computational requirements scale well with the number of sensors and the number of people, and only limited amounts of data are transmitted over the network. Because of these properties, our system runs in real-time and can be deployed as an untethered wireless sensor network. We describe the major components of our system, and report preliminary experiments with our first prototype implementation. 1.
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov models
- In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR
, 2005
"... Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both t ..."
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Cited by 81 (9 self)
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Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the sharedstructure HHMM, the estimation of the model’s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM. 1
Automated multi-camera planar tracking correspondence modeling
- Proc. of CVPR
, 2003
"... This paper introduces a method for robustly estimating a planar tracking correspondence model (TCM) for a large camera net-work directly from tracking data and for employing said model to reliably track objects through multiple cameras. By exploiting the unique characteristics of tracking data, our ..."
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Cited by 76 (4 self)
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This paper introduces a method for robustly estimating a planar tracking correspondence model (TCM) for a large camera net-work directly from tracking data and for employing said model to reliably track objects through multiple cameras. By exploiting the unique characteristics of tracking data, our method can re-liably estimate a planar TCM in large environments covered by many cameras. It is robust to scenes with multiple simultaneously moving objects and limited visual overlap between the cameras. Our method introduces the capability of automatic calibration of large camera networks in which the topology of camera overlap is unknown and in which all cameras do not necessarily overlap. Quantitative results are shown for a five camera network in which the topology is not specified. 1.
press) Modeling and Understanding Students’ Off-Task Behavior in Intelligent Tutoring Systems
- Proceedings of ACM SIGCHI: Computer-Human Interaction
, 2007
"... We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a learning task. This model was developed using only log files of system usage (i.e. no screen capture or a ..."
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Cited by 66 (22 self)
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We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a learning task. This model was developed using only log files of system usage (i.e. no screen capture or audio/video data). We show that this model can both accurately identify each student’s prevalence of off-task behavior and can distinguish off-task behavior from when the student is talking to the teacher or another student about the subject matter. We use this model in combination with motivational and attitudinal instruments, developing a profile of the attitudes and motivations associated with offtask behavior, and compare this profile to the attitudes and motivations associated with other behaviors in intelligent tutoring systems. We discuss how the model of off-task behavior can be used within interactive learning environments which respond to when students are off-task.
A self-organizing approach to background subtraction for visual surveillance applications
- Image Processing, IEEE Transactions on
"... Abstract—Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus ..."
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Cited by 57 (0 self)
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Abstract—Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science. The proposed approach can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, has no bootstrapping limitations, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras. We compare our method with other modeling techniques and report experimental results, both in terms of detection accuracy and in terms of processing speed, for color video sequences that represent typical situations critical for video surveillance systems. Index Terms—Background subtraction, motion detection, neural network, self organization, visual surveillance. I.
Robust Detection and Tracking of Human Faces with an Active Camera
, 2000
"... We present an efficient framework for the detection and tracking of human faces with an active camera. The Bhattacharyya coefficient is employed as a similarity measure between the color distribution of the face model and face candidates. The proper derivation of these distributions allows the use o ..."
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Cited by 45 (1 self)
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We present an efficient framework for the detection and tracking of human faces with an active camera. The Bhattacharyya coefficient is employed as a similarity measure between the color distribution of the face model and face candidates. The proper derivation of these distributions allows the use of the spatial gradient of the Bhattacharyya coefficient to guide a fast search for the best face candidate. The optimization, which is based on mean shift analysis, requires only a few iterations to converge. Scale changes of the trackedfaceare handled by exploiting the scale invariance of the similarity measure and the luminance gradient computed on the border of the hypothesized face region. The detection and tracking modules are almost identical, the difference being that the detection involves mean shift optimization with multiple initializations. Our dual-mode implementation of the camer controller determines the pan, tilt, and zoom camera to switch between smooth pursuit and saccadic movements, as a function of the target presence in the fovea region. The resulting system runs in real-time on a standard PC, being robust to partial occlusion, clutter, facescale variations, rotations in depth, and fast changes in subject/camera position.
