By Andrea Prati, Roberto Vezzani, Michele Fornaciari (auth.), Pradeep K. Atrey, Mohan S. Kankanhalli, Andrea Cavallaro (eds.)

Intelligent multimedia surveillance issues the research of a number of sensing inputs together with video and audio streams, radio-frequency identity (RFID), and intensity facts. those info are processed for the automatic detection and monitoring of individuals, cars, and different gadgets. The aim is to find relocating pursuits, to appreciate their habit, and to observe suspicious or irregular actions for crime prevention. regardless of its advantages, there's societal apprehension in regards to the use of such know-how, so a major problem during this study quarter is to stability public defense and privateness.

This edited publication provides fresh findings within the box of clever multimedia surveillance rising from disciplines comparable to multimedia computing, desktop imaginative and prescient, and synthetic intelligence. It comprises 9 chapters addressing clever video surveillance, video research of crowds, privateness concerns in clever multimedia surveillance, RFID expertise for localization of items, item monitoring utilizing visible saliency info, estimating multiresolution intensity utilizing lively stereo imaginative and prescient, and function evaluate for video surveillance platforms.

The booklet could be of worth to researchers and practitioners engaged on similar difficulties in safety, multimedia, and synthetic intelligence.

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As a result, the current literature in understanding crowd motion is not ready to capture the motion pattern of an unstructured crowd scene where the motion of the crowd appears to be random [63]. Future research in this area requires localised modelling of crowd motion to capture different behaviours in unstructured crowded scenes. On the other hand, the understanding and modelling of crowd behaviour remains immature despite the considerable advances in human activity analysis. Progress in this area requires further advances in modelling or representation of a crowd event and recognition of these events in a natural environment.

Springer, Berlin (2012) 17. : Multi-view synchronization of human actions and dynamic scenes. In: Proceedings of the British Machine Vision Conference 2009, British Machine Vision Association, pp. 11 (2009) 18. : Multiple camera tracking of interacting and occluded human motion. Proc. IEEE 89(10), 1441–1455 (2001) 19. : Using social effects to guide tracking in complex scenes. In: Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 212–217 (2007) 20. : Multi person tracking within crowded scenes.

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