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