By Yunqian Ma, Gang Qian
From the streets of London to subway stations in long island urban, thousands of surveillance cameras ubiquitously acquire thousands of movies, usually working 24/7. How can such mammoth volumes of video information be saved, analyzed, listed, and searched? How can complex video research and structures autonomously realize humans and realize specific actions real-time? Collating and providing the most recent details Intelligent Video Surveillance: structures and Technology explores those matters, from basics precept to algorithmic layout and process implementation.
An built-in dialogue of key learn and applications
Written and edited by means of a set of specialists, the e-book offers state of the art applied sciences and structures in clever video surveillance. The ebook integrates key learn, layout, and implementation issues of clever video surveillance platforms and know-how into one finished reference. The chapters disguise the computational ideas at the back of the applied sciences and platforms and contain method implementation concerns in addition to examples of profitable purposes of those applied sciences.
Builds a starting place for destiny developments
Changing visual appeal because of altering viewpoints, illumination, expression, and flow, self/cross physique occlusion, modeling of cluttered history in a position to effective heritage subtraction for item detection, and spatial and temporal alignment of a number of cameras are only a number of the demanding situations that stay in additional constructing and refining clever video surveillance know-how and platforms. totally illustrated with line artwork, tables, and images demonstrating the amassed video and effects acquired utilizing the similar algorithms, together with a colour plate part, the publication presents a high-level blueprint for advances and insights into destiny instructions of the sphere.
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Additional info for Intelligent Video Surveillance: Systems and Technology
Perpendicular to the gradient direction at the point). The intuition behind such a hysteresis procedure is that a true edge trace must have strong gradient somewhere along the edge (so that an edge can be detected using a high threshold), and that true edge points of the same edge should be 22 Intelligent Video Surveillance: Systems and Technology adjacent to each other along the edge direction (so that a started edge can be followed by finding neighbor edge points along the edge direction with gradient magnitudes above the low threshold).
23, where (xi, yi) and (x′i, y′i), i=1, …, n, are locations of a pair of corresponding points between the two camera views. To reliably estimate f, a critical step is data normalization (Hartley 1997). For a point cloud in each camera view, the following data normalization is carried out. First, shift the point cloud so it is centered at the origin, and then scale the shifted data points by a factor proportional to the average distance from the data points to their centroid. 24) where (x0, y0) is the centroid of points on the image, and s = √2/d, where d is the average distance from these data points to their centroid.
Real-valued vs. categorical output), and in a specific loss (discrepancy) measure L(y, f(x)) used. Hence, the specification of each learning problem quantifies the goal of learning and provides a measure of prediction accuracy, but it does not describe how to find a good model. These basic approaches are implemented in thousands of existing learning algorithms developed in machine learning, statistics, data mining, neural networks, pattern recognition, and signal processing. ) terminology. The general idea behind all modeling approaches is to specify a wide set of possible (admissible) models f(x, ω) indexed by some abstract set of parameters ω.