An active camera system for acquiring multi-view video

2005

T he importance of video surveillance techniques , , has increased considerably since the latest terrorist incidents. Safety and security have become critical in many public areas, and there is a specific need to enable human operators to remotely monitor activity across large environments such as: a) transport systems (railway transportation, airports, urban and motorway road networks, and maritime transportation), b) banks, shopping malls, car parks, and public buildings, c) industrial environments, and d) government establishments (military bases, prisons, strategic infrastructures, radar centers, and hospitals).

In this paper, we propose a new multiple-camera people tracking system that is equipped with the following functions: (1) can handle long-term occlusions, complete occlusions, and unpredictable motions; (2) can detect arbitrary sized foreground objects; (3) can detect objects with much faster speed. The main contribution of our method is twofold: 1) An Mto-one relationship with only point homography matching for occlusion detection can achieve efficiency; 2) A view-hopping technique based on object motion probability (OMP) is proposed to automatically select an appropriate observation view for tracking a human subject.

2006

In this paper, we present a coordinated video surveillance system that can minimize the spatial limitation and can precisely extract the 3D position of objects. To do this, our system used an agent based system and also tracked the normalized object using active wide-baseline stereo method.

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003

This paper presents a new approach for continuous tracking of moving objects observed by multiple, heterogeneous cameras. Our approach simultaneously processes video streams from stationary and Pan-Tilt-Zoom cameras. The detection of moving objects from moving camera streams is performed by defining an adaptive background model that takes into account the camera motion approximated by an affine transformation. We address the tracking problem by separately modeling motion and appearance of the moving objects using two probabilistic models. For the appearance model, multiple color distribution components are proposed for ensuring a more detailed description of the object being tracked. The motion model is obtained using a Kalman Filter (KF) process, which predicts the position of the moving object. The tracking is performed by the maximization of a joint probability model. The novelty of our approach consists in modeling the multiple trajectories observed by the moving and stationary cameras in the same KF framework. It allows deriving a more accurate motion measurement for objects simultaneously viewed by the two cameras and an automatic handling of occlusions, errors in the detection and camera handoff. We demonstrate the performances of the system on several video surveillance sequences.

ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), 2001

Recent innovations in real-time machine vision, distributed computing, software architectures, and high-speed communication are expanding the available technology for intelligent system development. These technologies allow the realization of intelligent systems that provide the capabilities for a user to experience events from remote locations in an interactive way. In this paper we describe research aimed at the realization of a powerful televiewing system applied to the traffic incident detection and monitoring needs of today's highways. Sensor clusters utilizing both rectilinear and omni-directional cameras will provide an interactive, real-time, multi-resolution televiewing interface to emergency response crews. Ultimately, this system will have a direct impact on reducing incident related highway congestion by improving the quality of information to which emergency personnel have access.

Real-Time Imaging, 1998

Real-Time Tracking of Moving Objects with an Active Camera T his article is concerned with the design and implementation of a system for real-time monocular tracking of a moving object using the two degrees of freedom of a camera platform. Figure-ground segregation is based on motion without making any a priori assumptions about the object form. Using only the first spatiotemporal image derivatives, subtraction of the normal optical flow induced by camera motion yields the object image motion. Closed-loop control is achieved by combining a stationary Kalman estimator with an optimal Linear Quadratic Regulator. The implementation on a pipeline architecture enables a servo rate of 25 Hz. We study the effects of time-recursive filtering and fixed-point arithmetic in image processing and we test the performance of the control algorithm on controlled motion of objects.

Ambient Intelligence

We present the IBM Smart Surveillance System that uses a distributed architecture to manage a heterogeneous network of active cameras. This system consists of a distributed network of cameras, each with local processing that interprets video to detect and track moving objects. The system performs multi-camera tracking as objects pass through the fields of view of different cameras, and actively acquires rich, highresolution data by actively tracking objects of interest with Pan-Tilt-Zoom cameras. The multi-resolution data is stored in a shared index that can be browsed and searched live or post-hoc from a remote location, visualizing very low bandwidth video or activity meta data.

In this paper, we propose a real-time video-surveillance system for image sequences acquired by a moving camera. The system is able to compensate the background motion and to detect mobile objects in the scene. Background compensation is obtained by assuming a simple translation of the whole background from the previous to the actual frame. Dominant translation is computed on the basis of the tracker proposed by Shi-Tomasi and Tomasi-Kanade. Features to be tracked are selected according to a new intrinsic optimality criterion. Badly tracked features are rejected on the basis of a statistical test. The current frame and the related background, after compensation, are processed by a change detection method in order to obtain a binary image of moving points.Results are presented in the contest of a visual-based system for outdoor environments.

SMPTE Motion Imaging Journal, 2007

In order to insert a virtual object into a TV image, the graphics system needs to know precisely how the camera is moving, so that the virtual object can be rendered in the correct place in every frame. Nowadays this can be achieved relatively easily in postproduction, or in a studio equipped with a special tracking system. However, for live shooting on location, or in a studio that is not specially equipped, installing such a system can be difficult or uneconomic. To overcome these limitations, the MATRIS project is developing a real-time system for measuring the movement of a camera. The system uses image analysis to track naturally occurring features in the scene, and data from an inertial sensor. No additional sensors, special markers, or camera mounts are required. This paper gives an overview of the system and presents some results.

2001

Multiple cameras are needed to cover large environments for monitoring activity. To track people successfully in multiple perspective imagery, one needs to establish correspondence between objects captured in multiple cameras. We present a system for tracking people in multiple uncalibrated cameras. The system is able to discover spatial relationships between the camera fields of view and use this information to correspond between different perspective views of the same person. We employ the novel approach of finding the limits of field of view (FOV) of a camera as visible in the other cameras. Using this information, when a person is seen in one camera, we are able to predict all the other cameras in which this person will be visible. Moreover, we apply the FOV constraint to disambiguate between possible candidates of correspondence. We present results on sequences of up to three cameras with multiple people. The proposed approach is very fast compared to camera calibration based approaches.