Recent progress in computation has enabled more efficient detection, data association, and tracking algorithms for multiple target tracking in multi-sensor environments. Data association filters are used when the origins of sensor measurements are unknown. The target measurements will appear only with a detection probability less than 1. In such an environment, the task of data association filters together with detection is to decide on the number and the presence of the targets and to estimate their trajectories. When tracking multiple targets in dense clutter environments, multi-sensor information is helpful to extend the surveillance region in addition to enhancing detection and tracking accuracies. However, these multi-sensor information fusion environments require efficient methods for detection, data association, tracking, and information fusion.
This Special issue invites technical contributions to the Sensors Special issue on “Multi-sensor Fusion for Object Detection and Tracking”. The Special Issue aims to provide an up-to-date overview of multi-sensor information fusion, object detection, data association, and tracking methods. The potential topics include but are not limited to novel computationally efficient multi-sensor fusion, detection, data association and tracking algorithms, and analysis for performance limits of the existing methods in terms of available computational resources.
Topics include, but not limit to:
multi-sensor information fusion technologies (homogeneous or heterogeneous)
multitarget tracking in clutter
computationally efficient detection and data association
track before detect
multiple-detection and multiple-path tracking