SURVANT: An Innovative Semantics-Based Surveillance Video Archives Investigation Assistant

Vella, G.; Dimou, Anastasios; Gutierrez-Perez, D.; Toti, Daniele; Nicoletti, Tommaso; Mattina, Ernesto La; Grassi, Francesco; Ciapetti, Andrea; McElligott, Michael; Shahid, Nauman; Daras, Petros

doi:10.1007/978-3-030-68787-8_44

Cited by 1 publication

(2 citation statements)

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“…Within the realm of formal languages for activity description, several noteworthy works have emerged, including ECA (Event-Condition-Action), ADeL (Activity Description Language), VIGILANT, SURVANT, CPNs (Colored Petri Nets), and ILIAD (Interactive Learning from Activity Description) [31][32][33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

“…The VIGILANT model combines object-oriented techniques and Description Logics (DLs) for efficient storage and retrieval of surveillance video content and events, enhancing semantic indexing capabilities. SURVANT is an innovative video archive investigation system that utilizes deep learning technologies for object recognition, tracking, and activity detection, enabling semantic indexing for efficient search and retrieval [35,36]. Conversely, ILIAD represents an interactive learning protocol that enables agents to undergo verbal training by describing their actions.…”

Section: Related Workmentioning

confidence: 99%

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A State-Based Language for Enhanced Video Surveillance Modeling (SEL)

Ramirez-Rosales,

Diaz-Jimenez,

Canton-Enriquez

et al. 2024

Modelling

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SEL, a State-based Language for Video Surveillance Modeling, is a formal language designed to represent and identify activities in surveillance systems through scenario semantics and the creation of motion primitives structured in programs. Motion primitives represent the temporal evolution of motion evidence. They are the most basic motion structures detected as motion evidence, including operators such as sequence, parallel, and concurrency, which indicate trajectory evolution, simultaneity, and synchronization. SEL is a very expressive language that characterizes interactions by describing the relationships between motion primitives. These interactions determine the scenario’s activity and meaning. An experimental model is constructed to demonstrate the value of SEL, incorporating challenging activities in surveillance systems. This approach assesses the language’s suitability for describing complicated tasks.

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