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

TL;DR

X-MAS is a comprehensive large-scale multi-modal dataset designed for outdoor surveillance, featuring diverse sensor data under challenging conditions to facilitate advanced deep learning research.

Contribution

The paper introduces the first large-scale first-person view outdoor multi-modal surveillance dataset with over 500,000 annotated image pairs across various sensor modalities.

Findings

Dataset enables robust surveillance algorithm development.

Multi-modal data improves detection and tracking accuracy.

Baseline deep learning methods demonstrate dataset's utility.

Abstract

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.