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We propose a real-time method for counting pedestrians and bicyclists by classifying bulks of asynchronous events generated upon scene activities by an event-based 3D dynamic vision system. The inherent detection of moving objects offered by the 3D dynamic vision system comprising a pair of dynamic vision sensors allows event-based stereo vision in real-time and a 3D representation of moving objects...
This paper proposes a real-time implementation of a clustering and classification method using asynchronous events generated upon scene activities by an event-based dynamic stereo vision system. The inherent detection of moving objects offered by the dynamic stereo vision system comprising a pair of dynamic vision sensors allows event-based stereo vision in real-time and a 3D representation of moving...
We present a visual computing effort to realistically and interactively simulate and visualize aspects of human motion behavior in virtual 3D environments. It allows virtually changing the infrastructure of a layout and assessing the consequences in terms of motion paths and visibility (where will people look at?). We first create a virtual 3D model of an infrastructure with photogrammetric reconstruction...
Video footage of real crowded scenes still poses severe challenges for automated surveillance. This paper evaluates clustering methods for finding independent dominant motion fields for an observation period based on a recently published real-time optical flow algorithm. We focus on self-tuning spectral clustering and Isomap combined with k-means. Several combinations of feature vector normalizations...
Capacities of doors, staircases and other bottle-necks are a key aspect in the design of infrastructures for public transport. Especially major events like soccer games and concerts may lead to large crowds which need to be accommodated, while at the same time potential safety hazards like overcrowding must be avoided. The bottleneck capacities limit the capacities of the whole system and control...
We propose a novel algorithm to find highly frequented paths of motion trajectories obtained from video sequences. This is achieved by representing the motion trajectories in the scene as sequences of prototypes obtained by a combined vector quantization and growing neural gas algorithm. In contrast to existing methods, the proposed algorithm can be applied to data sets containing motion trajectories...
We present a case study for obtaining and analyzing long-term pedestrian track data within a large hall of an Austrian railway station, where no CCTV surveillance system was pre-installed. Hence one focus of this paper concerns practical aspects for selecting and strategically placing a high-quality multi-camera system for recording long-term video data for off-line analysis. The pedestrian tracks...
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