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.
翻译:在机器人和计算机视觉社区,对监视任务进行了广泛的广泛研究,包括人类探测、跟踪和用相机识别,此外,上述任务和其他计算机视觉任务都广泛采用深学习算法;现有的公共数据集不足以开发以学习为基础的方法,对户外和极端情况,例如恶劣天气和低光度条件进行各种监视;因此,我们推出一个新的大型户外监视数据集,名为eXtremely 大型多式多式传感器数据集(X-MAS),包含50多万对图像和第一人视图数据,由经过良好训练的警告员附加说明。此外,一对单对包含多模式数据(如IR图像、RGB图像、热图像、深度图像和LDAR扫描)。这是第一个大型第一人观看室多式数据集,重点是我们最了解的监视任务。我们介绍拟议的数据集概览,以及目前利用基于深学习的自动识别器数据集利用我们的数据集的方法。通过MAR-comb/服务器进行最新数据研究,通过httpsse-comb/combs