A Hidden Markov Model based transitional description of camera networks (original) (raw)

Learning Higher-order Transition Models in Medium-scale Camera Networks

2007 IEEE 11th International Conference on Computer Vision, 2007

We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures.

Automatic pan-tilt-zoom calibration in the presence of hybrid sensor networks

2005

Wide-area context awareness is a crucial enabling technology for next generation smart buildings and surveillance systems. It is not practical to cover an entire building with cameras, however it is difficult to infer missing information when there are significant gaps in coverage. As a solution, we advocate a class of hybrid perceptual systems that builds a comprehensive model of activity in a large space, such as a building, by merging contextual information from a dense network of ultra-lightweight sensor nodes with video from a sparse network of high-capability sensors. In this paper we explore the task of automatically recovering the relative geometry between a pan-tilt-zoom camera and a network of one-bit motion detectors. We present results for the recovery of geometry alone, and also recovery of geometry jointly with simple activity models. Because we don't believe a metric calibration is necessary, or even entirely useful for this task, we formulate and pursue the novel goal we term functional calibration. Functional calibration is the blending of geometry estimation and simple behavioral model discovery. Accordingly, results are evaluated in terms of the ability of the system to automatically foveate targets in a large, non-convex space, not in terms of pixel reconstruction error.

Multi-Camera Scene Analysis using an Object-Centric Continuous Distribution Hidden Markov Model

2007 IEEE International Conference on Image Processing, 2007

We propose a multi-camera event detection framework that can operate on a common ground plane as well as on the image plane. The proposed event detector is based on an object-centric state modeling that uses a Continuous Distribution Hidden Markov Model (CDHMM). Video objects are first detected using statistical change detection and then tracked using graph matching. Next, the algorithm recognizes events by estimating the most likely object state sequence using a HMM decoding strategy, based on the Viterbi algorithm. We demonstrate and evaluate the proposed framework on standard event detection datasets with single and multiple cameras, with both overlapping and non-overlapping fields of view.

Parameter estimation of multi-dimensional hidden Markov models - a scalable approach

2005

Parameter estimation is a key computational issue in all statistical image modeling techniques. In this paper, we explore a computationally efficient parameter estimation algorithm for multi-dimensional hidden Markov models. 2-D HMM has been applied to supervised aerial image classification and comparisons have been made with the first proposed estimation algorithm. An extensive parametric study has been performed with 3-D HMM and the scalability of the estimation algorithm has been discussed. Results show the great applicability of the explored algorithm to multi-dimensional HMM based image modeling applications.

Tracking and Surveillance in Wide-Area Spatial Environments Using the Abstract Hidden Markov Model

International Journal of Pattern Recognition and Artificial Intelligence

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

Unsupervised Calibration of Camera Networks an Virtual PTZ Cameras

2012

Pan-Tilt-Zoom (PTZ) cameras are widely used in video surveillance tasks. In particular, they can be used in combination with static cameras to provide high resolution imagery of interesting events in a scene on demand. Nevertheless, PTZ cameras only provide a single trajectory at a time. Hence, engineering algorithms for common computer vision tasks, such as automatic calibration or tracking, for camera networks including PTZ cameras is difficult. Therefore, we propose a virtual PTZ (vPTZ) camera to simplify the algorithm development for such camera networks. The vPTZ camera is built on a cylindrical panoramic view of the scene and allows to reposition its field of view arbitrarily to provide several trajectories. Further, we propose an unsupervised extrinsic self-calibration method for a network of static cameras and PTZ cameras solely based on correspondences between tracks of a walking human. Our experimental results show that we can obtain accurate estimates of the extrinsic camera parameters in both, outdoor and indoor scenarios.

Look there! Predicting where to look for motion in an active camera network

2005

A framework is proposed that answers the following question: if a moving object is observed by one camera in a pan-tilt-zoom (PTZ) camera network, what other camera(s) might be foveated on that object within a predefined time window, and what would be the corresponding PTZ parameter settings? No calibration is assumed, and there are no restrictions on camera placement or initial parameter settings. The framework accrues a predictive model over time.

Topology Free Hidden Markov Models: Application to Background Modeling

2002

Hidden Markov Models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, realworld applications are often non-stationary in nature. This leads to the need for a dynamic mechanism to learn and update the model topology as well as its parameters. This paper presents a new framework for HMM topology and parameter estimation in an online, dynamic fashion. The topology and parameter estimation is posed as a model selection problem with an MDL prior. Online modifications to the topology are made possible by incorporating a state splitting criterion. To demonstrate the potential of the algorithm, the background modeling problem is considered. Theoretical validation and real experiments are presented.

Unsupervised Calibration of Camera Networks and Virtual PTZ Cameras

ABSTRACT Pan-Tilt-Zoom (PTZ) cameras are widely used in video surveillance tasks. In particular, they can be used in combination with static cameras to provide high resolution imagery of interesting events in a scene on demand. Nevertheless, PTZ cameras only provide a single trajectory at a time. Hence, engineering algorithms for common computer vision tasks, such as automatic calibration or tracking, for camera networks including PTZ cameras is difficult. Therefore, we propose a virtual PTZ (vPTZ) camera to simplify the algorithm development for such camera networks. The vPTZ camera is built on a cylindrical panoramic view of the scene and allows to reposition its field of view arbitrarily to provide several trajectories. Further, we propose an unsupervised extrinsic self-calibration method for a network of static cameras and PTZ cameras solely based on correspondences between tracks of a walking human. Our experimental results show that we can obtain accurate estimates of the extrinsic camera parameters in both, outdoor and indoor scenarios.