<title>Research environment for developing and testing object tracking algorithms</title>

Abstract

We present an integrated research environment (RAVEN) that we have developed for the purpose of developing and testing object tracking algorithms. As a Windows application, RAVEN provides a user interface for loading and viewing video sequences and interacting with the segmentation and object tracking algorithms, which are included at run time as plug-ins. The plug-ins interact with RAVEN via a programming interface, enabling algorithm developers to concentrate on their ideas rather than on the user interface. Over the past two years, RAVEN has greatly enhanced the productivity of our researchers, enabling them to create a variety of new algorithms and extend RAVEN's capabilities via plug-ins. Examples include several object tracking algorithms, a live-wire segmentation algorithm, a methodology for the evaluation of segmentation quality, and even a mediaprocessor implementation of an object tracker. After implementing an algorithm, RAVEN makes it easy to present the results since it provides several mask display modes and output options for both image and video. We have found that RAVEN facilitates the entire research process, from prototyping an algorithm to visualization of the results to a mediaprocessor implementation.

A generic flexible and robust approach for intelligent real-time video-surveillance systems

“…Our goals are first to obtain an efficient system that can meet strong industrial requirements and second to have a system that allows researchers to develop new vision algorithms. Such a system must for example include evaluation facilities, such as in [4]. A computer cluster based approach with fast Ethernet network connections is the innovative solution proposed to address the increasing needs of computing power at an affordable cost.…”

“…The requirements for these systems are to be network-connected, multi-cameras, modular, the display must be user-friendly, the vision modules should be plug-and-play and the overall system must be highly reliable and robust. The work reported here has both research [4] [5] and industrial motivations. In this article we present a generic, flexible and robust approach for an intelligent real-time video-surveillance system.…”

A seamless modular image analysis architecture for surveillance systems

“…The requirements for these systems are to be network-connected, multi-cameras, modular, the display must be user-friendly, the vision modules should be plug-and-play and the overall system must be highly reliable and robust. The work reported here has both research [12,8,13] and industrial motivations. In this article we present a generic, flexible and robust approach for an intelligent real-time video-surveillance system.…”