Abstract-In this paper, we propose to show how video data available in standard CCTV transportation systems can represent a useful source of information for transportation infrastructure management, optimization and planning if adequately analyzed (e.g. to facilitate equipment usage understanding, to ease diagnostic and planning for system managers). More precisely, we present two algorithms allowing to estimate the number of people in a camera view and to measure the platform time-occupancy by trains. A statistical analysis of the results of each algorithm provide interesting insights regarding station usage. It is also shown that combining information from the algorithms in different views provide a finer understanding of the station usage. An end-user point of view confirms the interest of the proposed analysis.
Video security is becoming more and more important today, as the number of installed cameras can attest. There are many challenging commercial applications to monitor people or vehicle traffic. The work reported here has both research and commercial motivations. Our goals are first to obtain an efficient intelligent system that can meet strong industrial surveillance system requirements and therefore be real-time, distributed, generic and robust. Our second goal is to have a development platform that allows researchers to conceive and easily test new vision algorithms thanks to its modularity and easy setup. This paper focuses on the image analysis modules. It considers the different kind of inputs, algorithm models as well as delay and the need of generality.
Video security is becoming more and more important today. CCTV (closed circuit television), after broadcast television, is currently migrating from analogue to digital. At the same time, electronics have rapidly progressed in miniaturizing components and standardization initiatives have became popular in the IT world. Due to these innovations, it is now possible to deploy easily and rapidly CCTV in site for permanent or temporary uses. Examples of challenging surveillance applications are monitoring metro stations, detection of loitering or abandoned objects, etc. This chapter describes a practical implementation of a distributed surveillance system with emphasis on video transmission issues (acquisition, visualisation) and image processing necessary for useful event detection. The requirements for these systems are to be easy to use, robust and flexible. Our goals are to obtain efficiently implemented systems that can meet these strong industrial requirements. A computer cluster based approach with network connections is the innovative solution proposed. The main advantage of this approach is its flexibility. Since mobile objects are important in videosurveillance, these systems will include image analysis tools such as segmentation and object tracking. First we present the typical requirements of such a system besides the typical robustness of the analysis (e.g. low false alarm rate and low missed detection rate). We consider issues like the facility to deploy and administer network-connected real-time multicameras, with reusable modular and generic technologies. Then we analyze how to cope with the needs to integrate a solution with state-of-the-art technologies. As an answer we then propose a global system architecture and we describe its main features to explain each underlying module. To illustrate the applicability of the proposed system architecture in real case studies, we develop some scenarios of deployment for indoors or outdoors applications.
Video security is becoming more and more important today, as the number of installed cameras can attest. There are many challenging commercial applications to monitor people or vehicle traffic. The work reported here has both research and commercial motivations. Our goals are first to obtain an efficient intelligent system that can meet strong industrial surveillance system requirements and therefore be real-time, distributed, generic and robust. Our second goal is to have a development platform that allows researchers to imagine and easily test new vision algorithms thanks to its modularity and easy setup. Previous papers [4,7] dealt with the core architecture for handling such problems as heterogeneous inputs, encoding, distribution, and storage. This paper will focus more precisely on the image analysis modules. We will consider the different kind of inputs, algorithm models as well as optimisation of memory, delay and genericity needs.
In this article we present a generic, flexible and robust approach for an intelligent real-time video-surveillance system. A previous version of the system was presented in [1]. The goal of these advanced tools is to provide help to operators by detecting events of interest in visual scenes, highlighting alarms and computing statistics. The proposed system is a multi-camera platform able to handle different standards of video inputs (composite, IP, IEEE1394 ) and which can basically compress (MPEG4), store and display them. This platform also integrates advanced video analysis tools, such as motion detection, segmentation, tracking and interpretation. The design of the architecture is optimized to playback, display, and process video flows in an efficient way for video-surveillance applications. The implementation is distributed on a scalable computer cluster based on Linux and IP network. It relies on POSIX threads for multitasking scheduling. Data flows are transmitted between the different modules using multicast technology and under control of a TCP-based command network (e.g. for bandwidth occupation control). We report here some results and we show the potential use of such a flexible system in third generation video surveillance systems. We illustrate the interest of the system in a real case study, which is the indoor surveillance.
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