WCAM: smart encoding for wireless surveillance

Meessen, Jerome; Parisot, Christophe; Barz, Cédric Le; Nicholson, Didier; Delaigle, Jean-François

doi:10.1117/12.585468

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…We use the segmentation results as the ROI definition for each frame. Different methods exist for coding ROIs in JPEG 2000: shifting the wavelet samples [6], re-organizing the codestream data as done in [2] or combining both of these approaches as in our previous work [3]. In our case, we choose to organize the codestream of each frame with different quality layers.…”

Section: A Encodingmentioning

confidence: 99%

Scene analysis for reducing motion JPEG 2000 video surveillance delivery bandwidth and complexity

Meessen

Parisot

Desurmont

et al. 2005

IEEE International Conference on Image Processing 2005

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In this paper, we propose a new object-based video coding/transmission system using the emerging Motion JPEG 2000 standard [1] 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 [2][3]. 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.

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Section: A Encodingmentioning

confidence: 99%

Scene analysis for reducing motion JPEG 2000 video surveillance delivery bandwidth and complexity

Meessen

Parisot

Desurmont

et al. 2005

IEEE International Conference on Image Processing 2005

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“…For example every 4 seconds or 10 images like in [1]. The RRI must be coded with no ROI option and with a moderate bit rate compression.…”

Section: Updating Of the Remote Referencementioning

confidence: 99%

“…With the Maxshift method, no extra information about the shape of the mask is required for the decoder. The authors in [1] combined the two above techniques to encode the ROI mask. Their system allows in real-time capturing an image, performing a motion detection, encoding with JPEG2000.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

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Dynamic Background Segmentation for Remote Reference Image Updating within Motion Detection JPEG2000

Totozafiny

Patrouix

Luthon

et al. 2006

2006 IEEE International Symposium on Industrial Electronics

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We present in this paper a new system based on Motion JPEG2000 intended for road surveillance application. The system uses a reference image and consists in 4 processing steps, namely initialization phase where the first reference image is built, reference estimation, motion segmentation (foreground extraction, ROI mask), and JPEG2000 coding. A first order recursive filter is used to build a reference image that corresponds to the background image. The obtained background is sent to the decoder once for all. The reference image at the coder side is estimated according to a Gaussian mixture model. The remote reference image is updated when specific conditions are met. The updating remote reference is triggered according to the states of mobile objects in the scene (no, few or lot of mobiles). The motion detection given by classical background subtraction technique is performed in order to extract a binary mask. The motion mask gives the region of interest of the system. The JPEG2000 image coded with a ROI option is sent towards the decoder. The decoder receives, decodes the image and builds the implicit binary ROI mask. Then, the decoder builds the displayed image using the reference image, the current image and the mask.

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“…The typical problems of this approach are changes of illumination, phantom objects, camera vibrations, and other natural effects such as tree branches moving. In the system it is possible to use different reference image models representing the backgrounds (low pass temporal recursive filter, median filter, uni-modal Gaussian, mixture of Gaussians [14,15] and vector quantisation [16]). In the literature, the most basic background model is updated to take small illumination changes into account by:…”

Section: Segmentationmentioning

confidence: 99%

A general-purpose system for distributed surveillance and communication

Desurmont

Bastide

Czyz

et al. 2006

Intelligent Distributed Video Surveillance Systems

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Video security is becoming more and more important today. CCTV (closed circuit television), after broadcast television, is currently migrating from analogue to digital. At the same time, electronics have rapidly progressed in miniaturizing components and standardization initiatives have became popular in the IT world. Due to these innovations, it is now possible to deploy easily and rapidly CCTV in site for permanent or temporary uses. Examples of challenging surveillance applications are monitoring metro stations, detection of loitering or abandoned objects, etc. This chapter describes a practical implementation of a distributed surveillance system with emphasis on video transmission issues (acquisition, visualisation) and image processing necessary for useful event detection. The requirements for these systems are to be easy to use, robust and flexible. Our goals are to obtain efficiently implemented systems that can meet these strong industrial requirements. A computer cluster based approach with network connections is the innovative solution proposed. The main advantage of this approach is its flexibility. Since mobile objects are important in videosurveillance, these systems will include image analysis tools such as segmentation and object tracking. First we present the typical requirements of such a system besides the typical robustness of the analysis (e.g. low false alarm rate and low missed detection rate). We consider issues like the facility to deploy and administer network-connected real-time multicameras, with reusable modular and generic technologies. Then we analyze how to cope with the needs to integrate a solution with state-of-the-art technologies. As an answer we then propose a global system architecture and we describe its main features to explain each underlying module. To illustrate the applicability of the proposed system architecture in real case studies, we develop some scenarios of deployment for indoors or outdoors applications.

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WCAM: smart encoding for wireless surveillance

Cited by 3 publications

References 11 publications

Scene analysis for reducing motion JPEG 2000 video surveillance delivery bandwidth and complexity

Scene analysis for reducing motion JPEG 2000 video surveillance delivery bandwidth and complexity

Dynamic Background Segmentation for Remote Reference Image Updating within Motion Detection JPEG2000

A general-purpose system for distributed surveillance and communication

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