&lt;title&gt;Intelligent video storage of visual evidences on site in fast deployment&lt;/title&gt;

Desurmont, Xavier; Bastide, A.; Delaigle, Jean-François

doi:10.1117/12.542032

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this example, almost all possible persons paths can be predicated and confirmed thanks to all low-level semantic such as speed, location, etc. Now using software libraries presented in section 6, calibration and context tools shown in section 5, using the hardware described in section 4 coupled with the deployment system of [1] and [13], we can install and deploy a concrete system as following :…”

Section: Case Studymentioning

confidence: 99%

Real-time high-level video understanding using data warehouse

et al. 2006

Self Cite

View full text Add to dashboard Cite

High-level Video content analysis such as video-surveillance is often limited by computational aspects of automatic image understanding, i.e. it requires huge computing resources for reasoning processes like categorization and huge amount of data to represent knowledge of objects, scenarios and other models.This article explains how to design and develop a "near real-time adaptive image datamart", used, as a decisional support system for vision algorithms, and then as a mass storage system. Using RDF specification as storing format of vision algorithms meta-data, we can optimise the data warehouse concepts for video analysis, add some processes able to adapt the current model and pre-process data to speed-up queries. In this way, when new data is sent from a sensor to the data warehouse for long term storage, using remote procedure call embedded in object-oriented interfaces to simplified queries, they are processed and in memory data-model is updated. After some processing, possible interpretations of this data can be returned back to the sensor.To demonstrate this new approach, we will present typical scenarios applied to this architecture such as people tracking and events detection in a multi-camera network. Finally we will show how this system becomes a high-semantic data container for external data-mining.

show abstract

Section: Case Studymentioning

confidence: 99%