In Intelligent Video Systems, most of the recent advanced performance evaluation metrics perform a stage of mapping data between the system results and ground truth. This paper aims to review these metrics using a proposed framework. It will focus on metrics for events detection, objects detection and objects tracking systems.

This paper builds on an interactive streaming architecture that supports both user feedback interpretation, and temporal juxtaposition of multiple video bitstreams in a single streaming session. As an original contribution, we explain how these functionalities can be exploited to offer improved viewing experience, when accessing highresolution or multi-views video content through individual and potentially bandwidth-constrained connections. This is done by giving the client the opportunity to select interactively a preferred version among the multiple streams that are offered to render the scene. An instance of this architecture has been implemented extending the liveMedia streaming library and using the x264 video encoder. Automatic methods have been designed and implemented to generate the multiple versions of the streamed content. In a surveillance scenario, the versions are constructed by sub-sampling the original high resolution image, or by cropping the image sequence to focus on regions of interest, in a temporally consistent way. In a soccer game context, zoomed-in versions of far view shots are computed and offered as alternatives to the sub-sampled sequence. We demonstrate the feasibility and relevance of the approach through subjective experiments.

This paper tackles the challenge of interactively retrieving visual scenes within surveillance sequences acquired with fixed camera. Contrarily to today's solutions, we assume that no a-priori knowledge is available so that the system must progressively learn the target scenes thanks to interactive labelling of a few frames by the user. The proposed method is based on very low-cost features extraction and integrates relevance feedback, multiple-instance SVM classification and active learning. Each of these 3 steps runs iteratively over the session, and takes advantage of the progressively increasing training set. Repeatable experiments on both simulated and real data demonstrate the efficiency of the approach and show how it allows reaching high retrieval performances.

Many implementations of visual tracking have been proposed since many years. The lack of standard evaluation process has prevented fair comparison between them. In this paper, we simply propose to evaluate different particle filter methods in people tracking applications. We introduce an objective metric and give results according to different parameter variations. Finally, based on our evaluations, we can propose a new particle filter configuration that outperforms other current implementations.

In this paper, we propose a new object-based video coding/transmission system using the emerging motion JPEG 2000 standard for the efficient storage and delivery of video surveillance over low bandwidth channels. Some recent papers deal with JPEG 2000 coding/transmission based on the region of interest (ROI) feature and the multi-layer capability provided by this coding system. Those approaches allow delivering more quality for mobile objects (or ROI) than for the background when bandwidth is too narrow for a sufficient video quality. The method proposed here provides the same features while significantly improving the average bitrate/quality ratio of delivered video when cameras are static. We transmit only ROIs of each frame as well as an automatic estimation of the background at a lower frame rate in two separate motion JPEG 2000 streams. The frames are then reconstructed at the client side without the need of other external data. Our method provides both better video quality and reduced client CPU usage with negligible storage overhead. Video surveillance streams stored on the server are fully compliant with existing motion JPEG 2000 decoders.

Although many different types of technologies for information systems have evolved over the last decades (such as databases, video systems, the Internet and mobile telecommunication), the integration of these technologies is just in its infancy and has the potential to introduce "intelligent" systems. This paper describes the novelties of a video content analysis in a surveillance system, demonstrating the benefits for fast retrieval in huge video databases