Fuse image enhancement with a regularized correlation filter for target tracking of UAVs

Abstract

Objective The tracking task of unmanned aerial vehicles (UAVs) often encounters various complex scenes. Factors such as illumination change, image blur, and low resolution greatly affect the tracking performance, which causes difficulty in achieving stable and long-term tracking tasks. The previous target tracking algorithms mainly focus on the robust tracking task of UAVs under sufficient light and high resolution. Less research is available on the tracking task in low-light scenes. The existing tracking algorithms for low-light scenes typically add an independent pre-image enhancement module to achieve an "enhancement-before-tracking" architecture, which can deal with low-light scenes. However, this architecture isolates the perception of scene information using the regularization module in the correlation filter, which limits the adaptability of the tracking algorithm to the tracking scene. Method To solve the two problems mentioned above, a target tracking algorithm based on image enhancement using a regularized correlation filter for UAVs is proposed to achieve robust tracking of UAV targets in the case of light changes and low resolution. First, an adaptive image enhancement module is constructed, and an illumination quality criterion composed of brightness and brightness difference is proposed. This criterion can flexibly determine whether to enhance the image according to the size of the illumination quality criterion, which avoids the large reduction in the running speed caused by image enhancement processing for all images and the excessive processing of some normal illumination images. In this way, the performance of the algorithm is not decreased but increased. The improved image enhancement module can improve the insufficient light of the image in certain cases and ensure clarity. As a result, the image can provide more effective feature information for subsequent processing, which effectively enhances the performance of the algorithm. Second, the illumination quality factor obtained in the image enhancement module is introduced into the temporal regularization to constrain the difference between the tracking response values of the two frames, and the illumination information in the scene is fused to realize the dynamic constraint of the temporal regularization. This fusion further enhances the adaptability of the algorithm to complex scenes and improves the robustness of the algorithm. Finally, the alternating direction method of multipliers is used to optimize the objective function of the proposed algorithm. The closed-form solution of each parameter can be obtained with fewer iterations, which further enhances the performance of the algorithm. Result To comprehensively evaluate the performance of the proposed algorithm, sufficient experiments are conducted on two public UAV datasets. The UAVdark70 dataset is mainly for dark-light and low-resolution scenes, with few categories but a large amount of data. The difficulty lies in accurately tracking the target in low-light and low-resolution situations. The UAV123@10fps dataset is a large and comprehensive dataset, with comprehensive tracking scenes, rich tracking objects, and many types of challenge attributes. It is used to test the comprehensive capability of the algorithm. On the UAVdark70 dark-light dataset, the proposed tracking model ranks first in tracking accuracy and tracking success rate. Compared with the benchmark algorithm AutoTrack, the tracking accuracy and tracking success rate of the proposed model are increased by 5.7 and 4.3, respectively. The results show that the algorithm can run stably under dark-light conditions and has good adaptability to the environment. In the UAV123@10fps comprehensive dataset, this model ranks first in tracking accuracy and tracking success rate. Compared with the benchmark algorithm AutoTrack, the tracking accuracy and tracking success rate of the model are increased by 1.8 and 1.3, respectively. Therefore, the proposed algorithm can effectively track the target with the temporal regularization fused with the illumination quality factor in various complex situations, which reflects robustness and scene adaptability. The data of challenge attributes reveal the overall excellent performance of the algorithm on the UAV123@10fps dataset. Specifically, in the face of camera motion, fast movement, out of view, occlusion, similar objects, and other challenge attributes, the proposed algorithm ranks first, which fully reflects the scene adaptability and robustness of the algorithm. It fully embodies the scene adaptability and robustness of the algorithm. To particularly test the effect of the algorithm, ablation experiments are conducted on the UAVdark70 dataset. The results show that the two proposed modules can improve the algorithm, and the combination of the two modules maximizes the comprehensive performance of the algorithm. Compared with the baseline algorithm, the tracking accuracy and tracking success rate are increased by 5.7 and 4.3, respectively. However, given that the UAVdark70 dataset mainly contains dark-light images, image enhancement processing is required. Thus, the speed is 4.1 frame/s lower than that of AutoTrack, but the real-time requirement is still achieved. Conclusion For the target tracking task of UAVs in low-light and low-resolution scenes, a target tracking algorithm based on image enhancement using a regularized correlation filter for UAVs is proposed to enhance adaptability to low-light scenes. In this way, the UAV can work in various scenes for a longer time. The comprehensive experimental results indicate that the proposed algorithm can effectively deal with insufficient light. It can also improve the tracking performance of UAVs in complex scenes with sufficient light. Therefore, the algorithm can effectively adapt to different scenes and provide strong support for stable and sustained operation of UAVs.