Abstract
Vision based object tracking problem still a hot and important area of research specially when the tracking algorithms are performed by the aircraft unmanned vehicle (UAV). Tracking with the UAV requires special considerations due to the flight maneuvers, environmental conditions and aircraft moving camera. The ego motion calculations can compensate the effect of the moving background resulted from the moving camera. In this paper an optimized object tracking framework is introduced to tackle this problem based on particle filter. It integrates the calculated ego motion transformation matrix with the dynamic model of the particle filter during the prediction stage. Then apply the correction stage on the particle filter observation model which based on two kinds of features includes Haar-like Rectangles and edge orientation histogram (EOH) features. The Gentle AdaBoost classifier is used to select the most informative features as a preliminary step. The experimental results achieved more than 94.6% rate of successful tracking during different scenarios of the VIVID database in real time tracking speed.
Highlights
Tracking objects in Unmanned Aerial Vehicle (UAV) camera image had been an important area of research many years ago
All foregrounds and backgrounds have the same size of region of interest (ROI) ݓand h The features extraction step based on Haar rectangles (HR) and edge orientation histogram (EOH) is applied on all foregrounds and backgrounds to extract all candidate features
The tracking process starts with selecting the best features form a pool of Haar-like rectangles (HR) and edge orientation histogram features (EOH)
Summary
Tracking objects in Unmanned Aerial Vehicle (UAV) camera image had been an important area of research many years ago. The difficulty of the tracking will increase in case of tracking using UAV camera images. This is due to the moving of this platform which produce an ego motion effect between the moving object and the background. Not all the features components extracted by certain feature extraction method may be considered as an optimum feature to contribute in an object representation model For this reason, the proposed system performs a preliminary classification step to determine the best feature components using boosting algorithm [11]. The particles will be updated based on the dynamics of the target object, and per their measurements, they will be re-weighted.
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