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In robotics and computer vision communities, extensive studies have beenwidely conducted regarding surveillance tasks, including human detection,tracking, and motion recognition with a camera. Additionally, deep learningalgorithms are widely utilized in the aforementioned tasks as in other computervision tasks. Existing public datasets are insufficient to developlearning-based methods that handle various surveillance for outdoor and extremesituations such as harsh weather and low illuminance conditions. Therefore, weintroduce a new large-scale outdoor surveillance dataset named eXtremelylarge-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000image pairs and the first-person view data annotated by well-trainedannotators. Moreover, a single pair contains multi-modal data (e.g. an IRimage, an RGB image, a thermal image, a depth image, and a LiDAR scan). This isthe first large-scale first-person view outdoor multi-modal dataset focusing onsurveillance tasks to the best of our knowledge. We present an overview of theproposed dataset with statistics and present methods of exploiting our datasetwith deep learning-based algorithms. The latest information on the dataset andour study are available at https://github.com/lge-robot-navi, and the datasetwill be available for download through a server.

X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments