Mr. Rhalem Zouaoui Profile

Rhalem Zouaoui

at Thales Research & Technology

SPIE Involvement:

Author

Publications (2)

Proceedings Article | 21 October 2015 Paper

Embedded security system for multi-modal surveillance in a railway carriage

Rhalem Zouaoui, Romaric Audigier, Sébastien Ambellouis, François Capman, Hamid Benhadda, Stéphanie Joudrier, David Sodoyer, Thierry Lamarque

Proceedings Volume 9652, 96520C (2015) https://doi.org/10.1117/12.2194262

KEYWORDS: Video, Sensors, Analytics, Expectation maximization algorithms, Cameras, Embedded systems, Video surveillance, Acoustics, Feature extraction, 3D modeling

Read Abstract +

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of “unusual” audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer’s theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

Proceedings Article | 26 May 2011 Paper

Multi-sensor millimeter-wave system for hidden objects detection by non-collaborative screening

Rhalem Zouaoui, Romain Czarny, Frédéric Diaz, Antoine Khy, Thierry Lamarque

Proceedings Volume 8022, 802209 (2011) https://doi.org/10.1117/12.886452

KEYWORDS: Extremely high frequency, Imaging systems, Synthetic aperture radar, Modulation transfer functions, Sensors, Point spread functions, Antennas, Mirrors, Image quality, Detector development

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years