Computer Vision Pattern Recognition

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 learn and the commercial worlds lately. the purpose of this attempt is to extend safety and security in different program domain names reminiscent of nationwide safeguard, domestic and financial institution protection, site visitors tracking and navigation, tourism, and army functions. The video surveillance structures at the moment in use percentage one function: A human operator needs to visual display unit them continuously, therefore proscribing the variety of cameras and the world below surveillance and extending fee. A enhanced approach could have non-stop energetic caution services, capable of alert defense officers in the course of or perhaps prior to the occurring of against the law.

Existing automatic surveillance structures may 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 outside city atmosphere, the place it's not 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 process for out of doors environments, whereas making a choice on the $64000 matters procedure must do something about in life like surveillance situations. The aim of the examine provided during this publication is to construct structures which could deal successfully with those life like surveillance needs.


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

Example text

The number of co-trained examples, for sequences 1,2 and 3 respectively. The graphs for each sequence show the improvement in performance with the increase in the use of examples labeled by the co-training method. tion accuracy. We reduce the likelihood of such a scenario by forcing the detected region to be within the foreground regions as determined by the background mod- 42 3 OBJECT DETECTION AND CATEGORIZATION Fig. 5 Some classification results from sequence 1. Objects classified as pedestrians are shown in black bounding boxes and objects classified as vehicles are shown in white bounding boxes.

The GE algorithm assumed that the correspondence between the first two frames was known. Rangarajan and Shah [100] proposed the proximal uniformity constraint to perform motion correspondence. The assumption was that points followed smooth paths and covered small distances in small time intervals. A non-iterative greedy algorithm was used to assign correspondences. This algorithm allowed for occlusion and for missing point detection but did not allow for false detections. Chetverikov and Verestoy [16] also used the smoothness constraint for tracking by a three step algorithm.

The problem is to identify that the foreground region contains multiple objects and to determine the location of each object in the region. Since people usually move in groups, which results in frequent inter-object occlusion, therefore detecting and resolving inter-object occlusion is important for surveillance applications. • Occlusion of objects due to scene structures causes the objects to disappear completely for a certain amount of time, that is there is no foreground region representing such objects.

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