The contemporary trend in large scale enterprises, like public infrastructures or industrial plants, is to use architectures and platforms for video surveillance through installation of a network of cameras in critical sites and monitoring the captured video data. Video surveillance provides quality assurance of services and/or products (adherence to predefined procedures of a production), traffic management control (for high-dense urban areas), security/safety (prevention from actions that may lead to hazardous situations), crisis management in public areas (e.g., train stations, airports), or a series of other applications of high industrial/social impact. However, the current commercial video surveillance systems support mostly manual supervision, making them both inefficient and subjective.

The inefficiency stems from the fact that it is impossible for a human to continually concentrate on monitors that display different activities in different areas. The subjectivity arises from the fact that humans usually interpret the same visual information differently under different conditions. For this reason, methods, tools and algorithms that aim to detect and recognize high level concepts and their respective spatio-temporal and causal relations (to identify semantic video activities, actions and procedures) have been in the focus of the research community over the last years and many research efforts have been paid within the computer vision and machine learning communities.

The traditional approaches for event detection in videos assume well structured environments and they fail to operate in largely unsupervised way under adverse and uncertain conditions from those on which they have been trained. Another drawback of current methods, is the fact that they focus on narrow domains using specific concept detectors such as “human faces”, “cars”, “buildings”. This special issue seeks original high innovative research in the area of self configurable cognitive video supervision in several domains.

This Call for Papers was very well received, and we collected several high quality papers. A severe review process led to a selection of a number of very good papers, so that the Editor-in-Chief agreed to devote two Special Issues in order to include all the accepted papers.

This special issue consists of eleven (11) papers that cover most of the areas of computer vision research in towards events, actions and workflows analysis. The papers can be classified in three generic categories, papers that apply low-level image analysis for events’ detection, articles suitable for events’ classification and works dealing with knowledge representation tools for understanding the visual events.

The first category articles apply algorithms for features extraction and object tracking, all appropriate for identifying events and actions in videos. More specifically, the paper of A. Kriechbaum et al. applies a framework for unsupervised segmentation of moving objects in image sequences that does not require any restriction on the video content. The approach extracts the moving objects using a mesh-based combination of colour segmentation and motion segmentation using a feature point tracking. D. Roth et al. present a visual object tracking method which is then assessed for events’ detection. The tracker is able to “detect and monitor” multiple object classes in non-controlled visual environments using Bayesian per-pixel classification in real-time. Furthermore, a general new event metric is used to compare the proposed tracking scheme with the other tracking methods against ground truth of multiple public datasets. The next paper applies dynamic trackers’ re-initialization schemes for improving their performance in case of complicated visual scenarios (A. Doulamis). Finally, a humans’ gait recognition algorithm has been proposed by the work of I. Bouchrika et al.

The second category of articles includes schemes for analyzing and classifying the events. In particular, C. Simon proposes a method for recognizing visual events using a decision tree mechanism. On the other hand, a rule-based system that combines image/visual analysis for identifying events in metro stations is proposed by B. Krausz et al. Another specific event visual analysis system is presented by Wei-Ta Chu, suitable for tennis court events. The system combines knowledge of tennis rules with specialized image/video analysis algorithms. The work of N. Doulamis applies an innovative angle spectrum algorithm for detecting vehicles and then extracting information about their behaviors in the roads as far as the car safety is concerned. Finally, implicit human actions annotation on large multimedia database is the theme of the last paper in this class.

Finally, in the last category we include knowledge representation papers for visual events detection. In particular, Minh-Son Dao et. al proposes a new spatio-temporal method for adaptively detecting events based on Allen temporal algebra. The temporal information is captured by presenting events as the temporal sequences using a lexicon of non-ambiguous temporal patterns. Finally, Senem Velipasalar et al. presents a tool for spatiotemporal event detection that lets users specify semantically high-level and composite events, and then detects their occurrences automatically. Events can be defined on a single camera view or across multiple camera views.

Guest Editors:

Anastasios Doulamis

Luc van Gool

Mark Nixon Professor

Nikolaos D. Doulamis

Prof. Theodora A. Varvarigou

Papers Order in the Special Issue

Please find below the preferred presentation order for the accepted papers in this special issue.

No. order

Corresponding author

Paper title

1

Andreas Kriechbaum

A Framework for Unsupervised Mesh based Segmentation of Moving Objects

2

Daniel Roth

Multi-Object Tracking Evaluated on Sparse Events

3

Anastasios Doulamis

Dynamic Tracking Re-Adjustment: A Method for Automatic Tracking Recovery in Complex Visual Environments

4

Imed Bouchrika

Performance Analysis for Automated Gait Extraction and Recognition in Multi-Camera Surveillance

5

Cédric Simon

Visual Event Recognition using Decision Trees

6

Barbara Krausz

MetroSurv: Detecting Events in Subway Stations

7

Wei-Ta Chu

Modeling Spatiotemporal Relationships between Moving Objects for Event Tactics Analysis in Tennis Videos

8

Nikolaos Doulamis

Coupled Multi-Object Tracking and Labeling for Vehicle Trajectory Estimation and Matching

9

Klimis Ntalianis,

Human Action Annotation, Modeling and Analysis based on Implicit User Interaction

10

Minh-Son Dao

A New Spatio-Temporal Method for Event Detection and Personalized Retrieval of Sports Video

11

Senem Velipasalar

Detection of User-defined, Semantically High-level, Composite Events, and Retrieval of Event Queries