ADVISOR - Annotated Digital Video for Surveillance and Optimised Retrieval
Overview
Background & policy context:
The strong political drive within Europe to encourage use of public transport will lead to increased passenger flows through metro stations and other critical elements of the public transport infrastructure. To meet this demand, public transport operators must improve the efficiency and security of their operations. The use of computer vision techniques will enhance the role of CCTV in meeting these objectives. The ADVISOR system builds on recent advances in computer technology applied to CCTV, academic research into computer vision and, in particular, progress made in recent European funded programs which address the needs of public transport operators. As they plan to upgrade their installations there is a growing problem of 'information overload' with more CCTV cameras installed that can be effectively monitored by the staff available).
Furthermore, facilities for recording CCTV data are either non-existent or very rudimentary. There is considerable scope for better exploitation of CCTV data to meet the growing demands on public transport operators for improvements in safety and efficiency. The ADVISOR project started from some work on previous EC projects, but was the first to integrate the results of advanced video processing algorithms with an archive, search and retrieval database and a human computer interface.
Objectives:
The goal of ADVISOR was to improve the management of public transport networks through better exploitation of data from CCTV cameras.
The principal objective of ADVISOR was to demonstrate the benefits of computer vision for reducing the workload on metro staff who rely on CCTV surveillance to assist them in the management of the transport network.
Two further objectives were to develop new management services for the operators, based on CCTV surveillance, and to demonstrate how a system such as ADVISOR should be specified and procured in the future.
The specific goals for the ADVISOR project were to demonstrate the following capabilities:
- Computer-assisted incident detection
- Post-incident analysis
- Quantitative analysis of behaviours
- Assessment and evaluation
- Market acceptability.
Methodology:
The results of previous work in this area will be used to identify the 'technology gaps' where current systems cannot meet user expectations and indicate how the latest technologies can be integrated to address these gaps. Functional specifications for ADVISOR will be developed, implemented and evaluated through two test-beds and one demonstrator to provide:
- Real-time computer analysis of multiple video sequences to detect, track and locate individuals in the 3D environment. It will be based on techniques for motion detection, calibration of cameras, scene modelling and object tracking. In addition, crowd-monitoring algorithms developed by CROMATICA will be integrated.
- Real time interpretation of the behaviour of individuals and of crowds to recognise anomalous or dangerous situations (such as overcrowding or vandalism) which would require a prompt response from metro staff to maintain smooth running of the transport network. This will be achieved by techniques for event detection and scenario recognition. Moreover, learning techniques will be developed to set thresholds for detecting anomalous situations and prioritising alarms to minimise operator workload.
- Tools for the creation, maintenance and searching of a digital video database, using appropriate image coding standards for storing compressed video with automatic annotation according to content, and to assist both the extraction of statistical information on passenger behaviour and also analysis of serious incidents after they have occurred.
- An ergonomic human computer interface for the display of annotated live video and diagnostics relating to analysis of activities and events that have been detected. The interface will also provide means for the operator to define alarm conditions, verify alarms that are generated and to access the video database. The implementation will use standard commercial hardware configured in an open and scalable architecture.
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