文献类型：期刊文献

英文题名：Dynamic Fusion Network for RGBT Tracking

作者：Peng, Jingchao[1];Zhao, Haitao[1];Hu, Zhengwei[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Automat Dept, Shanghai 200237, Peoples R China

年份：2023

卷号：24

期号：4

起止页码：3822

外文期刊名：IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

收录：;EI(收录号：20230313396246);WOS:【SCI-EXPANDED(收录号:WOS:001011287800016)】；

基金：This work was supported by the National Natural Science Foundation of China (NSFC) underGrant 62173143 and Grant 61973122.

语种：英文

外文关键词：Object tracking; fusion tracking; dynamic convolution; deep learning; intelligent perception

摘要：Since both visible and infrared images have their own advantages and disadvantages, RGBT tracking plays an important role in intelligent transportation systems. The key points of RGBT tracking lie in feature extraction and fusion of visible and infrared images. Current RGBT tracking methods mostly pay attention to both individual features (features extracted from images of a single camera) and common features (features extracted and fused from an RGB camera and a thermal camera). Still, they pay less attention to different and dynamic contributions of the individual and common features for different sequences of registered image pairs. This paper proposes a novel RGBT tracking method, called Dynamic Fusion Network (DFNet), which adopts a two-stream structure, in which two non-shared convolution kernels are employed in each layer to extract individual features. Besides, DFNet has shared convolution kernels for each layer to extract common features. Since non-shared and shared convolution kernels are adaptively weighted and summed according to different image pairs, DFNet can deal with different contributions for different sequences. DFNet has a fast speed, which is 28.658 FPS. The experimental results show that when DFNet only increases the Mult-Adds by 0.02% compared with the non-shared-convolution-kernel-based fusion method, Precision Rate (PR) and Success Rate (SR) reach 88.1% and 71.9%, respectively. The model and dataset are available at https://github.com/PengJingchao/DFNet.