Abstract
Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues, we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration, hypotheses generation, and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visual- ization and enable users to provide filter-based relevance feedback. Additionally, users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making, we gather uncertainties introduced by the computer vision step, communicate these information to users through uncertainty visualization, and grant fuzzy hypothesis formulation to interact with the machine. Finally, we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009.
Highlights
Tracking devices mechanistically capture individual movement as trajectories that consist of a series of positioning fixes
There are four main insights that can be drawn from the evaluation: 1) semantic trajectory compression (STC) compresses trajectories to only a fraction of raw data volumes; 2) it drastically reduces the amount of data to be stored compared to a trajectory’s network representation; 3) for purposeful movement through a transport network, with increasing length of trajectories, compression increases; the longer a trajectory is, the better is the compression rate; 4) STC reconstructs compressed trajectories featuring all essential information
Increasing compression rates with increasing length of trajectories can be explained with the number of direction changes along a path, i.e., those turns at an intersection that are not classified as “straight.” For the tested trajectories, the ratio between the number of nodes in the path corresponding to the trajectory and the number of direction changes is essentially constant over all groups of Figure 7
Summary
Tracking devices mechanistically capture individual movement as trajectories that consist of a series of positioning fixes. Humans plan, perceive, and communicate journeys as legs traveled in geographic space, typically following a street network or using train or bus lines. In urban environments there are few alternatives to following available transportation infrastructure network links. This paper exploits such semantic grounding of urban movement for compressing trajectory data. The paper addresses the GIScience priority of bridging the “gulf between low-level observational data (fixes) and high-level conceptual schemes (journeys) through which we as humans interpret, understand, and use that data” The paper addresses the GIScience priority of bridging the “gulf between low-level observational data (fixes) and high-level conceptual schemes (journeys) through which we as humans interpret, understand, and use that data” ( [16], p. 300)
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