Target tracking in dynamic background using generalized regression neural network

In this paper, we present a new approach to track moving objects in videos having a dynamic background. At first, we apply an object detection algorithm that deals with the detection of real objects in a degraded video by separating them from turbulence-induced motions using a two-level thresholding technique. Then, a generalized regression neural network is used to track the detected objects throughout the frames in the video. The proposed approach utilizes the features of centroid and area of moving objects and creates the reference regions instantly by selecting the objects within a circle. The performance of the proposed approach is compared with that of an existing approach by applying them to turbulence degraded videos, and competitive results are obtained.