On 3D model construction by fusing heterogeneous sensor data
CVGIP: Image Understanding
Optimal pose estimation in two and tree dimensions
Computer Vision and Image Understanding
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
A framework for heading-guided recognition of human activity
Computer Vision and Image Understanding
EMBOT: an enhanced motion-based object tracker
Journal of Systems and Software
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Successive elimination algorithm for motion estimation
IEEE Transactions on Image Processing
Hi-index | 12.05 |
This paper proposes a new computer-aided tracking method that can track multiple flying targets in an image sequence acquired from either an optical camera or an infrared camera system. The proposed method includes two major steps, which are flying target detection and flying target tracking. Because weather conditions greatly affect the detection of a flying target, we designed a fuzzy system that classifies the weather into two basic weather types. Based on the weather type, flying targets can be detected successfully. In tracking a flying target, we apply the dynamic layer representation method to represent the flying target and use a Kalman filter to predict the target's position. The second fuzzy rule system is designed to identify the target states, which are visible, missing, or occluded, based on the layer and positional features. Thereafter, the identified state can help to select a suitable tracking strategy for the subsequent frames. In the experiments, we demonstrate the results applied to five real image sequences. The proposed method can successfully detect and track multiple flying targets perfectly in clear weather. It also achieves around 90% accuracy when the weather is not clear. The fuzzy methodology makes the proposed system more effective at detecting and tracking targets and more flexible to complex environmental conditions, since the rules can be easily added and modified.