Mark Borg

Video understanding for complex activity recognition

This paper presents a real-time video understanding system which automatically recognises activities occurring in environments observed through video surveillance cameras. Our approach consists in three main stages: Scene Tracking, Coherence Maintenance, and Scene Understanding. The main challenges are to provide a robust tracking process to be able to recognise events in outdoor and in real applications conditions, to allow the monitoring of a large scene through a camera network, and to automatically recognise complex events involving several actors interacting with each others. This approach has been validated for Airport Activity Monitoring in the framework of the European project AVITRACK.

Visual Surveillance for Aircraft Activity Monitoring

This paper presents a visual surveillance system for the automatic scene interpretation of airport aprons. The system comprises two modules - scene tracking and scene understanding. The scene tracking module, comprising a bottom-up methodology, and the scene understanding module, comprising a video event representation and recognition scheme, have been demonstrated to be a valid approach for apron monitoring

Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance

Recent interest has been shown in performance evaluation of visual surveillance systems. The main purpose of performance evaluation on computer vision systems is the statistical testing and tuning in order to improve algorithms reliability and robustness. In this paper we investigate the use of empirical discrepancy metrics for quantitative analysis of motion segmentation algorithms. We are concerned with the case of visual surveillance on an airports apron, that is the area where aircrafts are parked and serviced by specialized ground support vehicles. Robust detection of individuals and vehicles is of major concern for the purpose of tracking objects and understanding the scene. In this paper, different discrepancy metrics for motion segmentation evaluation are illustrated and used to assess the performance of three motion segmentors on video sequences of an airports apron.

A Real-Time Scene Understanding System for Airport Apron Monitoring

This paper presents a distributed multi-camera visual surveillance system for automatic scene interpretation of airport aprons. The system comprises two main modules Scene Tracking and Scene Understanding. The Scene Tracking module is responsible for detecting, tracking and classifying the objects on the apron. The Scene Understanding module performs high level interpretation of the apron activities by applying cognitive spatio-temporal reasoning. The performance of the complete system is demonstrated for a range of representative test scenarios.

Automated scene understanding for airport aprons

This paper presents a complete visual surveillance system for automatic scene interpretation of airport aprons. The system comprises two main modules — Scene Tracking and Scene Understanding. The Scene Tracking module is responsible for detecting, tracking and classifying the semantic objects within the scene using computer vision. The Scene Understanding module performs high level interpretation of the observed objects by detecting video events using cognitive vision techniques based on spatio-temporal reasoning. The performance of the system is evaluated for a series of pre-defined video events specified using a video event ontology.

Video surveillance for aircraft activity monitoring

This paper presents a complete visual surveillance system for the automatic scene interpretation of airport aprons. The system comprises two modules scene tracking and scene understanding. The scene tracking module, comprising a bottom-up methodology, and the scene understanding module, comprising a video event representation and recognition scheme, have been demonstrated to be a valid approach for apron monitoring.

Evaluation of object tracking for aircraft activity surveillance

This paper presents the evaluation of an object tracking system that has been developed in the context of aircraft activity monitoring. The overall tracking system comprises three main modules - motion detection, object tracking and data fusion. In this paper we primarily focus on performance evaluation of the object tracking module, with emphasis given to the general 2D tracking performance and the 3D object localisation.

Robust Real-Time Tracking for Visual Surveillance

This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve tracking of objects that are in close proximity. The four component modules described in this paper are (i) motion detection using a layered background model, (ii) object tracking based on local appearance, (iii) hierarchical object recognition, and (iv) fused multisensor object tracking using multiple features and geometric constraints. This integrated approach to complex scene tracking is validated against a number of representative real-world scenarios to show that robust, real-time analysis can be performed.

Distributed multi-camera surveillance for aircraft servicing operations

This paper presents the visual surveillance aspects of a distributed intelligent system that has been developed in the context of aircraft activity monitoring. The overall tracking system comprises three main modules — Motion Detection, Object Tracking and Data Fusion. In this paper we primarily focus on the object tracking and data fusion modules.

Video event recognition for aircraft activity monitoring

This paper presents the approach taken to, and the results obtained for automatic scene interpretation of airport aprons based on a multi-camera video surveillance system. The scene tracking and scene understanding modules are described and results and evaluation are presented.