Chunsheng Hua

Search and Rescue Rotary-Wing UAV and Its Application to the Lushan Ms 7.0 Earthquake

Rapid search and rescue responses after earthquakes or in postseismic evaluation tend to be extremely difficult. To solve this problem, we summarized the requirements of search and rescue rotary-wing unmanned aerial vehicle SR-RUAV systems according to related works, manual earthquake search and rescue, and our knowledge to guide our research works. Based on these requirements, a series of research and technical works have been conducted to present an efficient SR-RUAV system. To help rescue teams locate interested areas quickly, a collapsed-building detecting approach that integrates low-altitude statistical image processing methods was proposed, which can increase survival rates by detecting collapsed buildings in a timely manner. The entire SR-RUAV system was illustrated by simulated earthquake response experiments in the China National Training Base for Search and Rescue CNTBSR from 2008 to 2010. On April 20, 2013, Lushan China experienced a disastrous earthquake magnitude 7.0. Because of the distribution of buildings in the rural areas, it was impossible to implement a rapid search and postseismic evaluation via ground searching. We provided our SR-RUAV to the Chinese International Search and Rescue Team CISAR and accurately detected collapsed buildings for ground rescue guidance at low altitudes. This system was significantly improved with respect to its searching/planning strategy and vision-based evaluation in different environments based on the lessons learned from actual missions after the earthquake. The SR-RUAV has proved to be applicable and time saving. The physical structure, searching and planning strategy, image-processing algorithm, and improvements in real missions are described in detail in this study.

K-means Tracker: A General Algorithm for Tracking People

In this paper, we present a clustering-based tracking algorithm for tracking people (e.g. hand, head, eyeball, body, and lips). It is always a challenging task to track people under complex environment, because such target often appears as a concave object or having apertures. In this case, many background areas are mixed into the tracking area which are difficult to be removed by modifying the shape of the search area during tracking. Our method becomes a robust tracking algorithm by applying the following four key ideas simultaneously: 1) Using a 5D feature vector to describe both the geometric feature “(x,y)” and color feature “(Y,U,V)” of each pixel uniformly. This description ensures our method to follow both the position and color changes simultaneously during tracking; 2) This algorithm realizes the robust tracking for objects with apertures by classifying the pixels, within the search area, into “target” and “background” with K-means clustering algorithm that uses both the “positive” and “negative” samples. 3) Using a variable ellipse model (a) to describe the shape of a nonrigid object (e.g. hand) approximately, (b) to restrict the search area, and (c) to model the surrounding non-target background. This guarantees the stable tracking of objects with various geometric transformations. 4) With both the “positive” and “negative” samples, our algorithm achieves the automatic self tracking failure detection and recovery. This ability makes our method distinctively more robust than the conventional tracking algorithms. Through extensive experiments in various environments and conditions, the effectiveness and the efficiency of the proposed algorithm is confirmed.

RK-Means Clustering: K-Means with Reliability

This paper presents an RK-means clustering algorithm which is developed for reliable data grouping by introducing a new reliability evaluation to the K-means clustering algorithm. The conventional K-means clustering algorithm has two shortfalls: 1) the clustering result will become unreliable if the assumed number of the clusters is incorrect; 2) during the update of a cluster center, all the data points belong to that cluster are used equally without considering how distant they are to the cluster center. In this paper, we introduce a new reliability evaluation to K-means clustering algorithm by considering the triangular relationship among each data point and its two nearest cluster centers. We applied the proposed algorithm to track objects in video sequence and confirmed its effectiveness and advantages.

Detection of collapsed buildings with the aerial images captured from UAV

In this paper, we present a method of detecting the collapsed buildings with the aerial images which are captured by an unmanned aerial vehicle (UAV) for the postseismic evaluation. Different from the conventional methods that apply the satellite images or the high-altitude UAV for the coarse disaster evaluation over large area, the purpose of this work is to achieve the accurate detection of collapsed buildings in small area from low altitude. By combining the motion and appearance features of collapsed buildings extracted from successive aerial images, each pixel in the input image will be measured by a statistical method where the background pixels will be penalized and the ones of collapsed buildings will be assigned with high value. The candidates of collapsed buildings will be established by integrating the extracted feature points into local groups with the online clustering algorithm. To reduce the false alarm caused by the complex background noise, each predicted candidate will be further verified by the temporal tracking framework where both the trajectory and the appearance of a candidate will be measured. The candidate of collapsed buildings that can survive through long time will be considered as true positive, otherwise rejected as a false alarm. Through extensive experiments, the efficiency and the effectiveness of proposed algorithm have been proved.摘要中文摘要本文提出了一种依靠低空无人机航拍图像进行坍塌建筑物自动识别的实时灾情评估方法。有别于传统的广域灾情粗略评估系统, 本方法依靠低空无人机实现了对村镇级别小范围区域的坍塌建筑物实时自动识别。本文创新点包括: 1) 结合航拍图像中每个像素点的外形和运动特征, 利用统计方法提取出坍塌建筑物上的有效特征点并抑制背景噪声; 2) 通过在线聚类算法实时提取出疑似坍塌建筑物区域; 3) 通过时空追踪算法对疑似区域进一步筛选, 排除误报结果。

Scale-invariant density-based clustering initialization algorithm and its application

In this paper, we bring out a new density-based clustering initialization algorithm which is invariant to the scale factor. Instead of using the scale factor while the cluster initialization, in this research, we determine the number and position of clusters according to the changes of cluster density with the division and agglomeration processes. During the division process, the initial cluster seeds are produced by a self-propagate method according to the density changes. The number of clusters is determined by agglomerating pair of RNN (reciprocal nearest neighbor) cluster seeds, when the density of newly merged cluster is increased. When no more cluster seeds can be merged any more, the remained number of cluster seeds is regarded as the real cluster number. Through various experiments, the effectiveness of the proposed algorithm has been proved.