Vehicle tracking and detection via feature point matching and adaptive sampling in aerial surveillance videos

• Main Authors: Chih-Chia Weng, 翁志嘉
• Other Authors: HSU-YUNG CHENG
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/b3ch24

博士 === 國立中央大學 === 資訊工程學系 === 105 === With rapid advance of Unmanned Aerial Vehicle (UAV) design and manufacture technologies, the analysis of aerial videos taken from aerial vehicle has become an important issue. It has a variety of applications, such as military, law enforcement, search and rescue, and traffic monitoring and management. One of the most important topics in aerial surveillance system is vehicle detection and tracking. In this study, we propose a vehicle tracking system for aerial surveillance videos. The experimental videos in this work are with low frame rate, low resolution, and variable altitude. To achieve our goal, we utilize Dynamic Bayesian Networks (DBNs) to detect vehicles in the initial step. And perform an adaptive detection model via Motion History Images (MHI) and corner images to detect vehicles during detection iteration. We also extract feature points on the detected vehicles for tracking. In the aerial videos with low contrast and low resolution, the feature points are not stable in successive frames. In order to solve this problem, we design a weighting scheme and incorporate the concept of Monte Carlo methods to update the vehicle feature points. In our proposed method, we perform particle sampling around the vehicle feature points and acquire the appearance models of the image patch at the sampled points. The sampling area is dynamically adjusted according to the matching conditions of vehicle feature points. The vehicle feature points are updated by the sampling point with the highest similarity measure with target appearance model. The weights of the feature points are also updated to give the confidence level of tracked feature points. Experimental results have validated that the proposed method can substantially improve the detection and tracking accuracy on a challenging dataset.