623 research outputs found
leave a trace - A People Tracking System Meets Anomaly Detection
Video surveillance always had a negative connotation, among others because of
the loss of privacy and because it may not automatically increase public
safety. If it was able to detect atypical (i.e. dangerous) situations in real
time, autonomously and anonymously, this could change. A prerequisite for this
is a reliable automatic detection of possibly dangerous situations from video
data. This is done classically by object extraction and tracking. From the
derived trajectories, we then want to determine dangerous situations by
detecting atypical trajectories. However, due to ethical considerations it is
better to develop such a system on data without people being threatened or even
harmed, plus with having them know that there is such a tracking system
installed. Another important point is that these situations do not occur very
often in real, public CCTV areas and may be captured properly even less. In the
artistic project leave a trace the tracked objects, people in an atrium of a
institutional building, become actor and thus part of the installation.
Visualisation in real-time allows interaction by these actors, which in turn
creates many atypical interaction situations on which we can develop our
situation detection. The data set has evolved over three years and hence, is
huge. In this article we describe the tracking system and several approaches
for the detection of atypical trajectories
Single Image Human Proxemics Estimation for Visual Social Distancing
In this work, we address the problem of estimating the so-called "Social
Distancing" given a single uncalibrated image in unconstrained scenarios. Our
approach proposes a semi-automatic solution to approximate the homography
matrix between the scene ground and image plane. With the estimated homography,
we then leverage an off-the-shelf pose detector to detect body poses on the
image and to reason upon their inter-personal distances using the length of
their body-parts. Inter-personal distances are further locally inspected to
detect possible violations of the social distancing rules. We validate our
proposed method quantitatively and qualitatively against baselines on public
domain datasets for which we provided groundtruth on inter-personal distances.
Besides, we demonstrate the application of our method deployed in a real
testing scenario where statistics on the inter-personal distances are currently
used to improve the safety in a critical environment.Comment: Paper accepted at WACV 2021 conferenc
Analysis-by-synthesis: Pedestrian tracking with crowd simulation models in a multi-camera video network
For tracking systems consisting of multiple cameras with overlapping field-of-views, homography-based approaches are widely adopted to significantly reduce occlusions among pedestrians by sharing information among multiple views. However, in these approaches, the usage of information under real-world coordinates is only at a preliminary level. Therefore, in this paper, a multi-camera tracking system with integrated crowd simulation is proposed in order to explore the possibility to make homography information more helpful. Two crowd simulators with different simulation strategies are used to investigate the influence of the simulation strategy on the final tracking performance. The performance is evaluated by multiple object tracking precision and accuracy (MOTP and MOTA) metrics, for all the camera views and the results obtained under real-world coordinates. The experimental results demonstrate that crowd simulators boost the tracking performance significantly, especially for crowded scenes with higher density. In addition, a more realistic simulation strategy helps to further improve the overall tracking result
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