Research on Image Moving Target Tracking Algorithm Based on Adaptive Feature Fusion in Complex Scenes

Abstract

Aiming at the problems of tracking drift or failure in target tracking for the scale change and fast motion, a image moving target tracking algorithm based on adaptive feature fusion in complex scenes is proposed. In this paper, the target classification module and target estimation module is designed respectively and combined effectively. In the target classification module, an adaptive feature fusion mechanism is designed, and it integrates multi-layer depth features so as to achieve effective online tracking. Moreover, the designed joint update strategy is more robust in dealing with motion blur and severe target deformation by optimizing the projection matrix layer and the correlation la-yer. In the target estimation module, the concept of IoU(Intersection over Union) maximization is introduced to predict the IoU score between bounding boxes and the estimation target. During the tracking process, the target state is estimated by using gradient ascent to maximize the IoU score to obtain a more accurate bounding box. Experimental results show that the proposed algorithm has excellent performance, with SAUC of 70.1%, 47.6%, 51.6% on OTB100, UAV123 and LaSOT datasets, which is superior to other related algorithms.

Keywords

• |target tracking|deep learning|target classification|target estimation|feature fusion