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.

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.