Uncertainty-aware video visual analytics of tracked moving objects

Abstract

There is an increasing number of rapidly growing repositories capturing the movement of people in space-time. Movement trajectory compression becomes an obvious necessity for coping with such growing data volumes. This paper introduces the concept of semantic trajectory compression (STC). STC allows for substantially compressing trajectory data with acceptable information loss. It exploits that human urban mobility typically occurs in transportation networks that define a geographic context for the movement. In STC, a semantic representation of the trajectory that consists of reference points localized in a transportation network replaces raw, highly redundant position information (e.g., from GPS receivers). An experimental evaluation with real and synthetic trajectories demonstrates the power of STC in reducing trajectories to essential information and illustrates how trajectories can be restored from compressed data. The paper discusses possible application areas of STC trajectories.

A conceptual framework and taxonomy of techniques for analyzing movement

“…In particular, these techniques are used for the reconstruction of events, movers, and trajectories from images (e.g. Turdukulov et al 2007, Höferlin et al 2009). …”

AlVis: Situation awareness in the surveillance of road tunnels

“…In their system for analyzing collections of audio and video recordings, Kubat et al [24] display one video channel at full resolution and up to ten video channels at reduced resolution. Höferlin et al [17] superimpose video sequences by extracted trajectory data which may also be filtered by their relevance. The same authors also support SA of CCTV operators by complementing the video display by an auditory display [16].…”

Interactive schematic summaries for exploration of surveillance video

“…Our approach is suitable for explorative browsing tasks, such as the inspection of video sequences with the objective to gain insight into structure, characteristics, and trends of object movements. In contrast to interactive query and filtering techniques [1,7], our method is far more exploration-oriented and can be used as initial browsing step prior to query approaches. Compared to recent methods that use summaries for navigation [17], playbackspeed adaption [8], or video synopsis, our approach allows much faster exploration of video data and offers higher scalascatter scatter playback scatter Figure 2: Browsing example for a video sequence from the AVSS'07 parked vehicle challenge 1 .…”

“…To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. approaches we find techniques for video browsing and navigation, such as navigation summaries [17], schematic storyboards [6], video synopsis [16], adaptive fast-forward [8], trajectory filtering [7], or interactive queries [1].…”