In video surveillance applications, pre-stored images are likely to be accessed remotely and interactively upon user request. In such a context, the JPEG 2000 still image compression format is attractive because it supports flexible and progressive access to each individual image of the pre-stored content, in terms of spatial location, quality level, as well as resolution. However, when the client wants to play consecutive frames of the video sequence, the purely INTRA nature of JPEG 2000 dramatically penalizes the transmission efficiency. To mitigate this drawback, conditional replenishment mechanisms are envisioned. They convey arbitrary spatio-temporal segments of the initial video sequence directly through sporadic and rate-distortion (RD) optimized refresh of JPEG 2000 packets. Hence, they preserve JPEG 2000 compliance, while saving transmission resources. The replenishment algorithms proposed in this paper are original in two main aspects. First, they exploit the specificities of the JPEG 2000 codestream structure to balance the accuracy (in terms of bit-planes) of the replenishment across image subbands in a (RD)-optimal way. Second, they take into account the still background nature of video surveillance content by maintaining two reference images at the receiver. One reference is the last reconstructed frame, as proposed in the original replenishment framework. The other is a dynamically computed estimate of the scene background, which helps to recover the background after a moving object has left the scene. As an additional contribution, we demonstrate that the embedded nature of the JPEG 2000 codestream easily supports prioritization of semantically relevant regions of interest while browsing video content. An interesting aspect of this JPEG 2000-based prioritization is that it can be regulatedaposteriori, after the codestream generation, based on the interest expressed by the user at browsing time. Simulation results demonstrate the efficiency and flexibility of the approach compared to INTER-based solutions.

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.

The image compression standard JPEG 2000 offers a high compression efficiency as well as a great flexibility in the way it accesses the content in terms of spatial location, quality level, and resolution. This paper explores how transmission systems conveying video surveillance sequences can benefit from this flexibility. Rather than transmitting each frame independently as it is generally done in the literature for JPEG 2000 based systems, we adopt a conditional replenishment scheme to exploit the temporal correlation of the video sequence. As a first contribution, we propose a rate-distortion optimal strategy to select the most profitable packets to transmit. As a second contribution, we provide the client with two references, the previous reconstructed frame and an estimation of the current scene background, which improves the transmission system 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.

The need for reversible or lossless watermarking methods has been highlighted in the literature to associate subliminal management information with losslessly processed media and to enable their authentication. The paper first analyzes the specificity and the application scope of lossless watermarking methods. It explains why early attempts to achieve reversibility are not satisfactory. They are restricted to well-chosen images, strictly lossless context and/or suffer from annoying visual artifacts. Circular interpretation of bijective transformations is proposed to implement a method that fulfills all quality and functionality requirements of lossless watermarking. Results of several bench tests demonstrate the validity of the approach.

Digital watermarking consists of hiding subliminal information into digital media content, also called host data. It can be the basis of many applications, including security and media asset management. In this paper, we focus on the imperceptibility requirement for image watermarking. We present the main features of the human visual system (HVS) to be translated into watermarking technology. This paper highlights the need for dedicated inputs from the human vision community. The human visual system (HVS) is very complex and able to deal with a huge amount of information. Roughly speaking, it is composed of a receiver with a pre-processing stage, the eye and the retina, a transmission channel, the optic nerve, and a processing engine, the visual cortex. Mainly because of our lack of knowledge about brain behavior, i.e. about the way a stimulus is processed through its huge neural network, the large effort to understand and model the HVS behavior has partly remained fruitless. The aim of this paper is not to provide a thorough description of the HVS. For complete HVS models and more specific details, the reader is referred to existing literature. Here, we only try to understand, in a synthetic way and from an engineering perspective, the HVS features on which the designer of a watermarking algorithm can rely, i.e. its sensitivity and masking capabilities.

Digital watermarking consists of hiding subliminal information into digital media content, also called host data. It can be the basis of many applications, including security and media asset management. In this paper we focus on the imperceptibility requirement for image watermarking. We first provide a functional inventory of image watermarking applications and emphasize the dependency between the application purpose and its need for invisibility. Then, we present a global framework common to most existing watermarking systems. It illustrates the methodology followed to translate human vision research into watermarking technology. It suggests future prospects and highlights the need for dedicated inputs from the human vision community.

This paper proposes some modifications to reference models of the human visual system for the benefit of a best performing fingerprinting scheme inside wavelet decompressors. Two new algorithms have been developed, based on more accurate modeling for luminance masking and on experimental tests for texture masking. In order to manifest the improvements, the insertion gain of the masks relative to non-perceptual watermarking schemes is calculated and compared to the one obtained by the reference technique. Also, the masks' energy distribution is checked to validate them as weighting functions of the watermark.

The need for reversible or lossless watermarking methods has been highlighted to associate information with losslessly processed media or to enable their authentication. The paper first analyzes the specificity and the application scope of lossless watermarking methods. An original circular interpretation of a bijective transformation is then proposed to implement a method that fulfill all quality and functionality requirements.

We present architectures for the secure delivery of images over open networks, such as the Internet or broadcast networks. Those systems integrate access control mechanisms and tracking procedures, once the pictorial material has been accessed. We show how these architectures have been tested in the context of the connection of cultural databases to the Internet (AQUARELLE system) and in the context of broadcasting of high-value TV programs (OCTALIS system used during the football World Cup). This work shows the interest for a global integrated design of delivery systems in which watermarking, monitoring, and public key infrastructures based on trusted third parties are designed according to coherent functional models.