At 07:49, 14 April 2010, the Yushu Tibetan Autonomous Prefecture of Qinghai Province on the Qinghai-Tibet Plateau, China was struck by a magnitude 7.1 earthquake, with the epicenter located at 33.1° N and 96.7° E and at an altitude of 4300 m, and an epicentral intensity of Modified Mercalli scale IX. It was the first strong earthquake that struck the high-altitude, hypoxia-prone Tibetan plateau primarily inhabited by ethnic minorities since the founding of the People’s Republic of China, which has caused a huge loss of lives and property and adversely impacted the economic and social development of the area. The 2010 Yushu Earthquake was an earthquake disaster with the greatest destruction, widest spatial extent, and greatest difficulty for relief efforts in the history of Yushu, involving 19 townships in six counties of the prefecture. As verified by the Ministry of Civil Affairs, Ministry of Public Security, and the Yushu Prefecture Government, the earthquake killed 2698 people and caused government agencies to list 270 missing persons, who were mostly in Jiegu Town of Yushu County. The earthquake also caused a direct economic loss of RMB 44 billion Yuan. The severe environmental conditions in Yushu and limited infrastructural support for disaster relief to remediate the impacts on the earthquake victims were also rare in the history of earthquake disaster relief. This article focuses on the characteristics of the high-altitude Yushu Earthquake assessment and response, and summarizes the experiences and lessons of government and society in responding to this earthquake. The assessment of and response to the Yushu Earthquake will provide helpful references for high plateau earthquake response in the future.
To track human across non-overlapping cameras in depression angles for applications such as multi-airplane visual human tracking and urban multi-camera surveillance,an adaptive human tracking method is proposed,focusing on both feature representation and human tracking mechanism.Feature representation describes individual by using both improved local appearance descriptors and statistical geometric parameters.The improved feature descriptors can be extracted quickly and make the human feature more discriminative.Adaptive human tracking mechanism is based on feature representation and it arranges the human image blobs in field of view into matrix.Primary appearance models are created to include the maximum inter-camera appearance information captured from different visual angles.The persons appeared in camera are first filtered by statistical geometric parameters.Then the one among the filtered persons who has the maximum matching scale with the primary models is determined to be the target person.Subsequently,the image blobs of the target person are used to update and generate new primary appearance models for the next camera,thus being robust to visual angle changes.Experimental results prove the excellence of the feature representation and show the good generalization capability of tracking mechanism as well as its robustness to condition variables.
