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

By Omar Javed

The deployment of surveillance platforms has captured the curiosity of either the study and the economic worlds lately. the purpose of this attempt is to extend safety and security in different software domain names resembling nationwide protection, domestic and financial institution protection, site visitors tracking and navigation, tourism, and armed forces functions. The video surveillance structures at present in use percentage one function: A human operator needs to video display them continually, hence proscribing the variety of cameras and the world below surveillance and lengthening expense. A improved method may have non-stop lively caution features, capable of alert safeguard officers in the course of or perhaps sooner than the taking place of a criminal offense.

Existing automatic surveillance platforms should be labeled into different types in accordance to:

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

The fundamental problem of this e-book is surveillance in an out of doors city atmosphere, the place it isn't attainable for a unmarried digital camera to watch the entire 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 procedure for out of doors environments, whereas making a choice on the real concerns method must take care of in sensible surveillance eventualities. The target of the examine provided during this booklet is to construct platforms which may deal successfully with those practical surveillance needs.

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The shape was modeled by training three Gaussian distributions,where there Gaussians modeled the head,torso and legs of the person. The correspondences were assigned using the maximum likelihood approach. The above mentioned methods assumed that a single person cannot belong to multiple regions. McKenna et. al. [83] used only color histograms to track people. Heuristics were used to merge two regions close to each other, thus a single person could belong to two regions. Since no motion or shape model is being used, there is a high probability of error if objects are similarly colored.

If the base classifiers are constructed such that each classifier is associated with a different feature, then the boosting mechanism will tend to select features that are not completely correlated. Note that, for cotraining we require two classifiers trained on separate features of the same data. In our case, individual base classifiers either represent motion features or appearance features. Thus, if the subset of base classifier selected through the boosting mechanism, representing only motion (or only appearance) features, confidently predicts the label of the data, then we can add this data to our training set to update the rest of the classifiers.

Ghosts objects are visible on uncovered background. (d) Edge and color combined results. The ghost objects were removed because their boundary pixels did not contain significant edges. objects. In this case, the camouflaged objects do not deviate significantly from the background model. One possible solution is to use both IR and EO sensors and employing R,G,B,IR information together as a feature vector for background subtraction. 26 2 IDENTIFYING REGIONS OF INTEREST IN IMAGE SEQUENCES Fig. 8 Removal of a static (background) object from the scene.

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