By Amit K. Roy-chowdhury, Bi Song
As networks of video cameras are put in in lots of purposes like defense and surveillance, environmental tracking, catastrophe reaction, and assisted dwelling amenities, between others, photo figuring out in digital camera networks is turning into a huge zone of study and know-how improvement. there are various demanding situations that have to be addressed within the technique. a few of them are indexed under: - conventional laptop imaginative and prescient demanding situations in monitoring and popularity, robustness to pose, illumination, occlusion, litter, acceptance of gadgets, and actions; - Aggregating neighborhood details for large zone scene knowing, like acquiring strong, long term tracks of items; - Positioning of the cameras and dynamic keep watch over of pan-tilt-zoom (PTZ) cameras for optimum sensing; - allotted processing and scene research algorithms; - source constraints imposed by way of assorted functions like safety and surveillance, environmental tracking, catastrophe reaction, assisted dwelling amenities, and so forth. during this ebook, we specialise in the fundamental examine difficulties in digicam networks, evaluate the present cutting-edge and current a close description of a few of the lately built methodologies. the key underlying subject in the entire paintings awarded is to take a network-centric view wherein the final judgements are made on the community point. this can be occasionally completed by way of collecting the entire info at a imperative server, whereas at different occasions by means of replacing judgements made via person cameras in keeping with their in the neighborhood sensed info. bankruptcy One begins with an summary of the issues in digicam networks and the main learn instructions. a number of the presently on hand experimental testbeds also are mentioned the following. one of many basic initiatives within the research of dynamic scenes is to trace gadgets. considering that digicam networks hide a wide zone, the platforms must be in a position to song over such vast components the place there might be either overlapping and non-overlapping fields of view of the cameras, as addressed in bankruptcy : disbursed processing is one other problem in digital camera networks and up to date tools have proven find out how to do monitoring, pose estimation and calibration in a allotted atmosphere. Consensus algorithms that allow those projects are defined in bankruptcy 3. bankruptcy 4 summarizes a couple of methods on item and job attractiveness in either allotted and centralized digicam community environments. some of these tools have concentrated totally on the research aspect on condition that pictures are being got via the cameras. effective usage of such networks usually demands energetic sensing, wherein the purchase and research levels are heavily associated. We speak about this factor intimately in bankruptcy 5 and convey how collaborative and opportunistic sensing in a digicam community could be completed. eventually, bankruptcy Six concludes the e-book by means of highlighting the most important instructions for destiny study. desk of Contents: An creation to digicam Networks / Wide-Area monitoring / allotted Processing in digital camera Networks / item and job attractiveness / lively Sensing / destiny examine instructions
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Extra resources for Camera Networks: The Acquisition and Analysis of Videos overWide Areas
Termination: The consensus process eventually ends with each agent contributing. 4. Integrity: Agents vote only once. Many consensus algorithms contain a series of events (and related messages) during a decisionmaking round. Typical events include Proposal and Decision. Here, proposal typically means the communication of the state of each agent and decision is the process of an agent deciding on proposals received from its neighbors. In our application domain of camera networks, the agents are the cameras and the state vector we are trying to estimate can be the position and velocity of a set of targets or the ID of an activity.
Two descriptors were considered: the histogram of oriented gradients in the Log-RGB color space (or HOG Log-RGB, in short), and the hue, saturation, and edgel values (or HSV-edgel, in short). HOG Log-RGB local descriptor. 17) where IR , IG , IB are the R, G, B channels of the image I and HOG(·, x) computes the histogram of oriented gradients on a region of w × w pixels around x. HSV-edgel local descriptor. In this descriptor, the color information is captured by the hue and saturation values of a pixel and the structure information is represented by a description of the pixel that has the characteristics of an edge, and is referred to as an edgel.
The FOV of C i is a rectangular pyramid in space with its tip at the optical center of the camera, and with its four sides passing through the lines (x = 0, x = xmax , y = 0, y = ymax ) on the image plane. The intersection of each planar side of this rectangular pyramid with the ground plane marks the boundaries of the footprint of the image. This is called a 3D FOV line. A projection of this 3D FOV line marking the limit of the footprint may be visible in another camera. The sides of a camera image can be defined by four lines, and s is used to denote an arbitrary side of these four.