The ARGOS campaign: Evaluation of Video Analysis Tools The benchmarking of various methods of video analysis and indexing has become a research problem per se. While a lot of effort nowadays is devoted to organization of evaluation campaigns both on international, such as TREC Video and national levels, an indeep analysis of the performance of the methods still remains an open issue. Several aspects in the evaluation of methods have to be addressed: how to design a benchmarking corpus in order to cover a sufficiently wide range of applications, what are the tasks to address, what are the most relevant evaluation metrics. The Argos evaluation campaign supported by the French TechnoVision program aimed at developing esources for a benchmarking of video content analysis and indexing methods. The paper describes the type of the evaluated tasks, the way the content set has been produced, metrics and tools developed for the evaluations and some of the results obtained at the end of the first phase. Perspectives based on current works will be given to conclude this paper.

A Fast and Efficient Framework for Indexing and Detection of Modified Copies in Video In this paper, we propose a framework for detecting near duplicate copies of a video based on an ordinal method. The framework also incorporates a bitmap indexing structure instead of a typical indexing structure used in the previous published work. Using this method, two levels of indices are constructed. The first level of this process groups each input video (represented by their key frames) into k clusters. These clusters and the associated key frames are then used to construct the first level index. The second level of this process converts ordinalbased video signatures (generated using the technique developed in earlier work) into bitmap vectors. By adopting this twolevel indexing scheme, query processing times are significantly reduced. This is because, the system is required to match only videos in the clusters that are relevant to the query and not all the videos in the database. Additionally, the technique implemented utilizes a bitmap structure for indexing, resulting in less storage space. Furthermore, we are able to employ lowcost Boolean operations such as AND, OR, and XOR in the matching process instead of Euclidean distance or other similar matching algorithms. This helps to reduce the computational time for video matching. The system has demonstrated to effectively reduce the space needed to store collections of video signatures in a database, as well as improving the overall system performance. In addition, initial results show that the system is effective and robust to several transformations such as changes in brightness, color, contrast, resolution (reduction) as well as the addition of noise.