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http://dx.doi.org/10.9717/kmms.2020.23.8.1006

Multi Modal Sensor Training Dataset for the Robust Object Detection and Tracking in Outdoor Surveillance (MMO (Multi Modal Outdoor) Dataset)  

Noh, DongKi (Advanced Robotics Lab. LG Electronics Inc.)
Yang, Wonkeun (Advanced Robotics Lab. LG Electronics Inc.)
Uhm, Teayoung (Korea Institute of Robotics & Technology Convergence)
Lee, Jaekwang (Advanced Robotics Lab. LG Electronics Inc.)
Kim, Hyoung-Rock (Advanced Robotics Lab. LG Electronics Inc.)
Baek, SeungMin (Advanced Robotics Lab. LG Electronics Inc.)
Publication Information
Journal of Korea Multimedia Society / v.23, no.8, 2020 , pp. 1006-1018 More about this Journal
Abstract
Dataset is getting more import to develop a learning based algorithm. Quality of the algorithm definitely depends on dataset. So we introduce new dataset over 200 thousands images which are fully labeled multi modal sensor data. Proposed dataset was designed and constructed for researchers who want to develop detection, tracking, and action classification in outdoor environment for surveillance scenarios. The dataset includes various images and multi modal sensor data under different weather and lighting condition. Therefor, we hope it will be very helpful to develop more robust algorithm for systems equipped with difference kinds of sensors in outdoor application. Case studies with the proposed dataset are also discussed in this paper.
Keywords
Outdoor Surveillance; Action Classification; Object Detection & Tracking; Mobile Robot; Multi Modal Sensor Dataset;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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