By Yunqian Ma, Gang Qian

From the streets of London to subway stations in manhattan urban, millions of surveillance cameras ubiquitously gather thousands of video clips, usually working 24/7. How can such huge volumes of video information be saved, analyzed, listed, and searched? How can complex video research and structures autonomously realize humans and observe particular actions real-time? Collating and proposing the newest info Intelligent Video Surveillance: platforms and Technology explores those concerns, from basics precept to algorithmic layout and method implementation.

An built-in dialogue of key learn and applications

Written and edited through a suite of specialists, the publication provides state of the art applied sciences and platforms in clever video surveillance. The e-book integrates key examine, layout, and implementation subject matters of clever video surveillance structures and know-how into one entire reference. The chapters hide the computational ideas at the back of the applied sciences and structures and contain process implementation matters in addition to examples of profitable purposes of those applied sciences.

Builds a origin for destiny developments

Changing visual appeal attributable to altering viewpoints, illumination, expression, and stream, self/cross physique occlusion, modeling of cluttered historical past able to effective history subtraction for item detection, and spatial and temporal alignment of a number of cameras are only many of the demanding situations that stay in additional constructing and refining clever video surveillance expertise and platforms. totally illustrated with line paintings, tables, and images demonstrating the amassed video and effects got utilizing the comparable algorithms, together with a colour plate part, the ebook presents a high-level blueprint for advances and insights into destiny instructions of the sphere.

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After discarding the first certain number of samples (the samples in the burn-in stage), the sample set can represent the distribution well. To guarantee that the stationary distribution of a Markov chain converges to the desired distribution, one sufficient condition for, that is, the detailed balance, Q(x0|x)P(x) = Q(x|x0)P(x0), where P(x) is the distribution we would want to sample from and Q(x0|x) is the transition probability of the Markov chain. Metropolis–Hasting algorithm is one generally used MCMC method.

However, the Kalman filter and its extension are based on the assumption of Gaussian distributions. For a general, non-Gaussian distribution probabilistic system, the inference problem can be achieved by a particle filter algorithm, which is derived from importance sampling. One of the particle filter algorithms is the CONDENSATION (conditional density propagation) algorithm (Isard and Blake 1998). Under the particle representation, any arbitrary probability distribution can be represented as a set of N weighted particles, s(tn), π (tn) , ∑ nN=1 π (tn) = 1.

In the realm of biometrics, gender and age estimation from video belongs to soft biometrics. Chapter 15 describes state-of-the-art techniques and systems in soft biometrics for video surveillance, including age estimation from face, gender recognition from face, and gender recognition from human body. Up to this point, intelligent video surveillance has been conducted using static cameras. Certain practical scenarios require video surveillance from moving cameras. Chapter 16 describes such a system for moving object detection and tracking from a UAV.

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