Feature-concatenated transformer for 3D object tracking in point clouds
Qi Shen, Zhiguang Shi, Yan Zhang, Yong Zhang · 2023
Feature fusion is a key problem in 3D object tracking, especially in sparse and disordered point clouds scenes. The purpose of feature fusion is to achieve the communication and integration of template features and search features, so as to obtain the fusion features with object-specific information. However, most pervious Transformer-based methods use the SelfAttention Module(SAM) and Cross-Attention Module(CAM) to conduct attention operations progressively in two steps, which is not conducive to focus on the discriminative features from the beginning. Benefiting from the flexibility of attention operations, we propose a Feature-Concatenated Attention Module (FCAM) for ego-feature enhancement and cross-feature augment at the same time. Based on FCAM, we propose a Feature-Concatenated Transformer (FCT) framework to explore more effective 3D object tracking method. This scheme is more useful to achieve deeper integration and extensive communication between template and search features, which makes feature fusion more efficient. In order to verify the performance of the proposed framework, we carried out experimental verification on KITTI datasets. The results of the experiment indicate that our method is superior to the existing schemes in tracking success and accuracy for different object categories.