AlVis: Situation awareness in the surveillance of road tunnels

Abstract

In the surveillance of road tunnels, video data plays an important role for a detailed inspection and as an input to systems for an automated detection of incidents. In disaster scenarios like major accidents, however, the increased amount of detected incidents may lead to situations where human operators lose a sense of the overall meaning of that data, a problem commonly known as a lack of situation awareness. The primary contribution of this paper is a design study of AlVis, a system designed to increase situation awareness in the surveillance of road tunnels. The design of AlVis is based on a simplified tunnel model which enables an overview of the spatiotemporal development of scenarios in real-time. The visualization explicitly represents the present state, the history, and predictions of potential future developments. Concepts for situation-sensitive prioritization of information ensure scalability from normal operation to major disaster scenarios. The visualization enables an intuitive access to live and historic video for any point in time and space. We illustrate AlVis by means of a scenario and report qualitative feedback by tunnel experts and operators. This feedback suggests that AlVis is suitable to save time in recognizing dangerous situations and helps to maintain an overview in complex disaster scenarios.

A Survey of Traffic Data Visualization

“…For instance, video surveillance [24] has been combined with road incident data for better monitoring. We believe that visual analysis of heterogeneous data from different sources (e.g., social media) will be the next research topic on data-driven ITS.…”

“…They cover situation-aware exploration and prediction [5], [14], [75], [91], [94], pattern discovery and clustering [9], [23], [34]- [36], [68], [92], and traffic situation monitoring [7], [11], [24], [26], [27], [93]. In this section, we present representative works, and summarize these works in Table III.…”

“…AIVis [24] is a system that monitors traffic situations in road tunnels. The incidents are detected automatically from video sequences in real time.…”

Visual Traffic Jam Analysis Based on Trajectory Data

“…Users are allowed to select, filter and cluster these incidents. Piringer et al [38] study the surveillance videos in a tunnel. They automatically detect and prioritize different types of events and mark them in space and time.…”

VisRuption: Intuitive and Efficient Visualization of Temporal Airline Disruption Data

“…The authors developed a tool which uses a linked view [Hen98] of the actual flood‐simulation space and a hierarchically branched time line to visualize the parallel possible worlds. Piringer et al [PBB12] propose a design to increase situation awareness for road tunnel surveillance. The system provides intuitive access to respective video data through an overview over the spatio‐temporal relations in the tunnel.…”