Papers by Author: Ying Hong Xie

Abstract: Fine particulate matter PM_2.5 is the main factor of "Haze” weather. The paper chose the content of various indicators in the sets of air quality index AQI detected by a monitoring station in Wuhan city as samples. Based on the methods of Spearman's rank correlation coefficient and Kendall rank correlation coefficient to establish mathematical model, the paper analyzed the correlation between the content of PM_2.5 and other five basic detection indicators and corresponding pollutants, with providing theoretical and experimental evidence for air management work. Two methods of the experiment results showed that PM_2.5 (content) has a strong correlation with sulfur dioxide, nitrogen dioxide, particulate matter, carbon monoxide (content), and had a negative correlation with ozone (content).

Abstract: In this paper, BP neural network model is used to establish the occurrence and evolution model of PM2.5 in an area in Xi'an city. In the model, wind, humidity, season, SO₂,NO₂,PM₁₀, CO,O₃ (in one hour ) and O₃ (in eight hours ) and other influence factors are all considered. The model has good reliability, it can accurately forecast the value of PM2.5 and its variation in the near future, which can provide the basis for the PM2.5 control.

Abstract: For mutual occlusion problem in multi-object tracking process, a novel tracking algorithm based on bilateral structure tensor corner detection is proposed, which can separate the objects correctly when they experience mutual occlusion. Firstly, it gains the information of each object corners. Secondly, when occlusion occurs, it makes use of K nearest neighbor algorithm combining with the nearest algorithm to classify the corners in occlusion region. Finally, the multi-object tracking algorithm is proposed. The experimental results show that the proposed method can separate the objects correctly and track the objects effectively, when they experience mutual occlusion, even the object changes its motion direction after occlusion.

Abstract: The existing object tracking method using covariance modeling is hard to reach the desired tracking performance when the deformation of moving target and illumination changes are drastic, we proposed a object tracking algorithm based on bilateral filtering. Firstly, the algorithm deals the image to be tracked with bilateral filtering, and extracts the needed features of filtered image to construct covariance matrix as tracking model. Secondly, under log-Euclidean Riemannian metric, we construct similarity measure for object covariance matrix and model updating strategy. Extensive experiments show that the proposed method has better adaptability for object deformation and illumination changes.

Abstract: Considering the process of objects imaging in the camera is essentially the projection transformation process. The paper proposes a novel visual tracking method using particle filtering on SL(3) group to predict the changes of the target area boundaries of next moment, which is used for dynamic model. Meanwhile, covariance matrices are applied for observation model. Extensive experiments prove that the proposed method can realize stable and accurate tracking for object with significant geometric deformation, even for nonrigid objects.