Download PDF by Omar Javed: Automated Multi-Camera Surveillance: Algorithms and Practice

By Omar Javed

ISBN-10: 0387788808

ISBN-13: 9780387788807

ISBN-10: 0387788816

ISBN-13: 9780387788814

The deployment of surveillance platforms has captured the curiosity of either the examine and the economic worlds in recent times. the purpose of this attempt is to extend safety and security in numerous program domain names akin to nationwide defense, domestic and financial institution defense, site visitors tracking and navigation, tourism, and armed forces functions. The video surveillance structures at the moment in use proportion one characteristic: A human operator needs to computer screen them continuously, therefore restricting the variety of cameras and the world lower than surveillance and extending price. A enhanced approach might have non-stop energetic caution features, in a position to alert protection officers in the course of or perhaps ahead of the occurring of a criminal offense.

Existing automatic surveillance structures may be categorised into different types in accordance to:

  • The atmosphere they're essentially designed to observe;
  • The variety of sensors that the automatic surveillance procedure can handle;
  • The mobility of sensor.

The fundamental main issue of this ebook is surveillance in an out of doors city environment, the place it isn't attainable for a unmarried digicam to monitor the whole niche. a number of cameras are required to monitor such huge environments. This e-book discusses and proposes thoughts for improvement of an automatic multi-camera surveillance process for out of doors environments, whereas choosing the $64000 concerns approach must deal with in life like surveillance situations. The aim of the examine awarded during this ebook is to construct structures that may deal successfully with those sensible surveillance needs.


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Additional info for Automated Multi-Camera Surveillance: Algorithms and Practice

Example text

The task of finding the region in current frame was formulated as follows: the feature representing the color or texture of the target region being tracked was assumed to have a density function, while a candidate region also had a feature distributed by a certain density. The problem was to find a candidate region whose associated density is most similar to the target density. A modified Bhattacharraya coefficient measure was used as a distance metric between the distributions. The approach adopted by Jepson et.

Such an approach basically assumes that the density of unlabeled examples must be low near the classification boundary. This makes good sense for problems where the classes are Gaussian or are generated by some other simple density function. However, this assumption often does not hold for image based object detection tasks, where the density of the required class often cannot be modelled by simple parametric models. The co-training approach to learn from unlabeled data was proposed by Blum and Mitchell [10].

7 Co-Training Ability Measurement We used Collaborative Ability (CA) Measurement [112] to determine the effectiveness of co-training framework in current setup of object classification . CA indicates whether two classifiers can co-train effectively. It quantifies the degree by which one classifier can reduce the classification uncertainty of other classifier. If CA is high than there is large portion of data for which one classifier is very confident and can provide such data to the other classifier and help improve its performance.

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Automated Multi-Camera Surveillance: Algorithms and Practice by Omar Javed

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