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.
Object tracking systems continue to be an intensive area of research, for which detection and processing of occlusion is a well-known challenge. This paper proposes a new approach to detection and handling of occlusion based on the integration of two known techniques, optical flow and particle filtering. Results of preliminary experiments show that the proposed method can detect and overcome the occlusion problem successfully during the tracking process.
Object tracking systems continue to be an intensive area of research, for which detection and processing of occlusion is a well-known challenge. This paper proposes a new approach to detection and handling of occlusion based on the integration of two known techniques, optical flow and particle filtering. Results of preliminary experiments show that the proposed method can detect and overcome the occlusion problem successfully during the tracking process.
Object tracking is an important task in several computer vision applications. Optical flow is one of the most widely used techniques in the image processing and video analysis fields. This paper implements an object tracking algorithm based on optical flow method to be computed by Raspberry Pi microcomputer. A Lucas-Kanade method has been used to calculate the velocity vector of the moving object between two consecutive frames. Two experiments are performed to evaluate the robustness of the proposed algorithm by the new computing device. The results were encouraging to use the proposed framework on wide variety of real time application.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.