Search

In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users 1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.

Mining object-level knowledge, that is, building a comprehensive category model base, from a large set of cluttered scenes presents a considerable challenge to the field of artificial intelligence. How to initiate model learning with the least human supervision (i.e., manual labeling) and how to encode the structural knowledge are two elements of this challenge, as they largely determine the scalability and applicability of any solution. In this article, we propose a model-learning method that starts from a single-labeled object for each category, and mines further model knowledge from a number of informally captured, cluttered scenes. However, in these scenes, target objects are relatively small and have large variations in texture, scale, and rotation. Thus, to reduce the model bias normally associated with less supervised learning methods, we use the robust 3D shape in RGB-D images to guide our model learning, then apply the properly trained category models to both object detection and recognition in more conventional RGB images. In addition to model training for their own categories, the knowledge extracted from the RGB-D images can also be transferred to guide model learning for a new category, in which only RGB images without depth information in the new category are provided for training. Preliminary testing shows that the proposed method performs as well as fully supervised learning methods.

For reasons of public security, an intelligent surveillance system that can cover a large, crowded public area has become an urgent need. In this article, we propose a novel laser-based system that can simultaneously perform tracking, semantic scene learning, and abnormality detection in a fully online and unsupervised way. Furthermore, these three tasks cooperate with each other in one framework to improve their respective performances. The proposed system has the following key advantages over previous ones: (1) It can cover quite a large area (more than 60×35m), and simultaneously perform robust tracking, semantic scene learning, and abnormality detection in a high-density situation. (2) The overall system can vary with time, incrementally learn the structure of the scene, and perform fully online abnormal activity detection and tracking. This feature makes our system suitable for real-time applications. (3) The surveillance tasks are carried out in a fully unsupervised manner, so that there is no need for manual labeling and the construction of huge training datasets. We successfully apply the proposed system to the JR subway station in Tokyo, and demonstrate that it can cover an area of 60×35m, robustly track more than 150 targets at the same time, and simultaneously perform online semantic scene learning and abnormality detection with no human intervention.