WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection
Citations Over TimeTop 10% of 2018 papers
Abstract
People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static multi-camera set-ups. Using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance. In this paper, we present a new large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated frames for detection at a rate of 2 frames per second. This results in over 40 000 bounding boxes delimiting every person present in the area of interest, for a total of more than 300 individuals. We provide a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.
Related Papers
- → Review on Microscopic Pedestrian Simulation Model(2016)53 cited
- Method for Improvement of Baseline Resolving Quality in GPS Measurement(2007)
- Analysis of the Baseline Decorrelation and Critical Baseline of Interferometric SAR(2003)
- Study and application of telecom operators system security state baseline(2012)
- Analysis on Influence of Design of Pedestrian Bridge to Pedestrian Traffic(2013)