By Amit K. Roy-chowdhury, Bi Song
As networks of video cameras are put in in lots of functions like safeguard and surveillance, environmental tracking, catastrophe reaction, and assisted residing amenities, between others, photograph realizing in digital camera networks is turning into a big quarter of study and know-how improvement. there are various demanding situations that must be addressed within the technique. a few of them are indexed under: - conventional computing device imaginative and prescient demanding situations in monitoring and popularity, robustness to pose, illumination, occlusion, muddle, popularity of items, and actions; - Aggregating neighborhood details for huge quarter scene realizing, like acquiring strong, long term tracks of items; - Positioning of the cameras and dynamic keep an eye on of pan-tilt-zoom (PTZ) cameras for optimum sensing; - allotted processing and scene research algorithms; - source constraints imposed through varied purposes like safety and surveillance, environmental tracking, catastrophe reaction, assisted dwelling amenities, and so on. during this booklet, we concentrate on the fundamental examine difficulties in digicam networks, evaluate the present state of the art and current an in depth description of a few of the lately constructed methodologies. the main 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 is often occasionally completed via gathering the entire facts at a critical server, whereas at different instances via changing judgements made by way of person cameras according to their in the neighborhood sensed information. bankruptcy One begins with an outline of the issues in digicam networks and the main learn instructions. the various at present to be had 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 sector, the platforms must be capable of tune over such large parts the place there should be either overlapping and non-overlapping fields of view of the cameras, as addressed in bankruptcy : dispensed processing is one other problem in digital camera networks and up to date tools have proven the way to do monitoring, pose estimation and calibration in a dispensed surroundings. Consensus algorithms that let those initiatives are defined in bankruptcy 3. bankruptcy 4 summarizes a number of techniques on item and task popularity in either disbursed and centralized digicam community environments. some of these equipment have targeted totally on the research aspect provided that photos are being got by means of the cameras. effective usage of such networks frequently demands energetic sensing, wherein the purchase and research levels are heavily associated. We talk about this factor intimately in bankruptcy 5 and express how collaborative and opportunistic sensing in a digicam community should be accomplished. eventually, bankruptcy Six concludes the publication by means of highlighting the main instructions for destiny learn. desk of Contents: An creation to digicam Networks / Wide-Area monitoring / dispensed Processing in digital camera Networks / item and job reputation / lively Sensing / destiny examine instructions
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Additional info for Camera Networks: The Acquisition and Analysis of Videos overWide Areas
Due to the nonlinear nature of the observation model, the linear Kalman-Consensus filter proposed in [Olfati-Saber and Sandell, 2008] cannot be applied as is. An extension to deal with the non-linearity of the observation model is required. The following are our basic Kalman filter iterations, as implemented in each camera, in the information form 1 : • Prediction P(k + 1) = A(k)M(k)A(k)T + B(k)Q (k)B(k)T , x¯ (k + 1) = A(k)ˆx (k). 3) • Correction M(k)−1 = P(k)−1 + H(k)T R(k)−1 H(k), K(k) = M(k)H(k)T R(k)−1 , xˆ (k) = x¯ (k) + K(k)(z(k) − h(¯x (k))).
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. 18) 26 2. WIDE-AREA TRACKING where IH and IS represent the hue and saturation, and e indicate the edgel.
In a camera network with non-overlapping 28 2. WIDE-AREA TRACKING fields of view, knowledge about the possible trajectories that targets can follow in the “blind” areas, can significantly improve the tracking performance. This has been referred to as the problem of learning a camera network topology (here topology refers to the traffic flow patterns, rather than the communication paths). Inference of the topology requires tracking; in turn, as more knowledge about the traffic patterns is gleaned from the data, the tracking performance should improve over time.