The Image Library for Intelligent Detection Systems (i-LIDS) provides benchmark surveillance datasets for analytics systems. This paper proposes a methodology to investigate the effect of compression and frame-rate reduction, and to recommend an appropriate suite of degraded datasets for public release. The library consists of six scenarios, including Sterile Zone (SZ) and Parked Vehicle (PV), which are investigated using two different compression algorithms (H.264 and JPEG) and a number of detection systems. PV has higher spatio-temporal complexity than the SZ. Compression performance is dependent on scene content hence PV will require larger bit-streams in comparison with SZ, for any given distortion rate. The study includes both industry standard algorithms (for transmission) and CCTV recorders (for storage). CCTV recorders generally use proprietary formats, which may significantly affect the visual information. Encoding standards such as H.264 and JPEG use the Discrete Cosine Transform (DCT) technique, which introduces blocking artefacts. The H.264 compression algorithm follows a hybrid predictive coding approach to achieve high compression gains, exploiting both spatial and temporal redundancy. The highly predictive approach of H.264 may introduce more artefacts resulting in a greater effect on the performance of analytics systems than JPEG. The paper describes the two main components of the proposed methodology to measure the effect of degradation on analytics performance. Firstly, the standard tests, using the ‘f-measure’ to evaluate the performance on a range of degraded video sets. Secondly, the characterisation of the datasets, using quantification of scene features, defined using image processing techniques. This characterization permits an analysis of the points of failure introduced by the video degradation.
The Image Library for Intelligent Detection Systems (i-LIDS) is the United Kingdom government's benchmark for Video
Analytics (VA) systems. There are currently 5 different scenario based datasets available. A new suite of datasets is
under development, intended to assess VA performance working with imagery obtained under Near Infra Red (NIR)
illumination conditions and from thermal imagers (infra red cameras). This paper describes the datasets that are under
construction. The datasets should be publically available in late 2010.
The Imagery Library for Intelligent Detection Systems (iLids) is the UK Government's standard for Video
Based Detection Systems (VBDS). The first four iLids scenarios were released in November 2006 and
annual evaluations for these four scenarios began in 2007.
The Home Office Scientific Development Branch (HOSDB), in partnership with the Centre for the
Protection of National Infrastructure (CPNI), has also developed a fifth iLids Scenario; Multiple Camera
Tracking (MCT). The fifth scenario data sets were made available in November 2008 to industry, academic
and commercial research organizations The imagery contains various staged events of people walking
through the camera views. Multiple Camera Tracking Systems (MCTS) are expected to initialise on a
specific target and be able to track the target over some or all of the camera views.
HOSDB and CPNI are now working on a sixth iLids dataset series. These datasets will cover several
• Thermal imaging systems
• Systems that rely on active IR illumination
The aim is to develop libraries that promote the development of systems that are able to demonstrate
effective performance in the key application area of people and vehicular detection at a distance.
This paper will:
• Describe the evaluation process, infrastructure and tools that HOSDB will use to evaluate MCT
systems. Building on the success of our previous automated tools for evaluation, HOSDB has
developed the MCT evaluation tool CLAYMORE. CLAYMORE is a tool for the real-time
evaluation of MCT systems.
• Provide an overview of the new sixth scenario aims and objectives, library specifications and
timescales for release.