**6. References**

54 Recent Developments in Video Surveillance

With the output of the second object signature building approach, the object matching is

Despite the fact that a number of surveillance video systems have been deployed, very few surveillance databases are available. One reason is that surveillance videos concern to human and organization privacy. Recently, several surveillance video databases such as CAVIAR, i-LIDS, CARETAKER have been released for research purpose. CAVIAR (Context Aware Vision using Image-based Active Recognition) is a project funded by the EC's Information Society Technology's programme project IST 2001 37540. This project addresses two surveillance applications: city centre surveillance and marketers. Corresponding to these applications, two databases are available. Video clips in the first database were filmed with a wide angle camera lens in the entrance lobby of the INRIA Labs at Grenoble (France) while those of the second database are filmed with a wide angle lens along and across the hallway in a shopping centre in Lisbon (Portugal). Moreover, videos of these databases are annotated. 2008 i-LIDS Multiple-Camera Tracking Scenario (MCTS) is a data set with multiple camera views from a busy airport arrival hall (Zheng, Gong et al. 2009). In the context of CARETAKER (Content Analysis and REtrieval Technologies to Apply Extraction to massive Recording), a video surveillance database is available. This project aims at studying, developing and assessing multimedia knowledge-based content analysis, knowledge extraction components and meta data management sub-systems in the context of automated situation awareness, diagnosis and decision support. During this project, a real testbed sites inside the metro of Roma and Torin, involving more than 30 sensors (20 cameras and 10 microphones) have been

In recent years, a number of surveillance video retrieval results have been published. However, with the lack of common benchmarks and databases, the comparison of these results is difficult (even impossible). Two preliminary comparisons of three object signature building and object matching methods with CAVIAR and CARETAKER dataset have been presented in (Le, Thonnat et al. 2009a) (Le, Thonnat et al. 2009). However, these

In this chapter, firstly a brief overview of surveillance object retrieval is given. Then, current work dedicated to appearance-based surveillance object retrieval are analysed in detail. The analysis shows that preliminary and promising results have been obtained for surveillance object retrieval. However, it is still a challenging issue. This issue needs more work and contributions on surveillance video analysis, feature extraction and common benchmark for

relatively simple.

**4.1 Databases** 

provided.

**5. Conclusions** 

**4. Surveillance object retrieval results** 

**4.2 Surveillance object retrieval results** 

surveillance object retrieval evaluation.

comparisons are done with a relatively small dataset.


**0**

**4**

*Poland*

**Quality Assessment in Video Surveillance**

Anyone who has experienced artifacts or freezing play while watching a film or a live sporting event on TV is familiar with the frustration accompanying sudden quality degradation at a key moment. Notwithstanding, video services with blurred images may have far more severe consequences for video surveillance practitioners. Therefore, the Quality of Experience (QoE) concept for video content used for entertainment differs considerably from the quality of video used for recognition tasks. This is because in the latter case subjective user satisfaction depends only or almost only on the possibility of achieving a given functionality (event detection, object recognition). Additionally, the quality of video used by a human observer for recognitions tasks is considerably different from objective video quality used in computer

So called, task-based videos require a special framework appropriate to the video's function — i.e. its use for recognition tasks rather than entertainment. Once the framework is in place, methods should be developed to measure the usefulness of the reduced video quality rather than its entertainment value. The precisely computed usefulness can be used to optimize not only the video quality but the whole surveillance system. It is especially important since surveillance systems often aggregates large number of cameras which streams has to be saved for possible future investigation. For example in Chicago at least 10,000 surveillance cameras

To develop accurate objective measurements (models) for video quality, subjective experiments must be performed. For this purpose, the ITU-T1 P.910 Recommendation "Subjective video quality assessment methods for multimedia applications" (ITU-T, 1999)

In this chapter the methodology for performing subjective tests is presented. It is shown that subjective experiments can be very helpful nevertheless they have to be correctly prepared and analyzed. For illustration of the problem, license plates recognition analysis is shown in

Some subjective recognition metrics, described below, have been proposed over the past decade. They usually combine aspects of Quality of Recognition (QoR) and QoE. These metrics have been not focused on practitioners as subjects, but rather on naïve participants.

addresses the methodology for performing subjective tests in a rigorous manner.

<sup>1</sup> International Telecommunication Union — Telecommunication Standardization Sector

**1. Introduction**

detail.

**2. Related work**

processing (Computer Vision).

are connected to a common storing system (ACLU, 2011).

Mikołaj Leszczuk, Piotr Romaniak and Lucjan Janowski

*AGH University of Science and Technology*

