The Siamese architecture has shown remarkable performance in the field of visual tracking. Although the existing Siamese-based tracking methods have achieved a relative balance between accuracy and speed, the performance of many trackers in complex scenes is often unsatisfactory, which is mainly caused by interference factors, such as target scale changes, occlusion, and fast movement. In these cases, excessive trackers cannot employ sufficiently the target feature information and face the dilemma of information loss. In this work, we propose a novel parallel Transformer network architecture to achieve robust visual tracking. The proposed method designs the Transformer-1 module, the Transformer-2 module, and the feature fusion head (FFH) based on the attention mechanism. The Transformer-1 module and the Transformer-2 module are regarded as corresponding complementary branches in the parallel architecture. The FFH is used to integrate the feature information of the two parallel branches, which can efficiently exploit the feature dependence relationship between the template and the search region, and comprehensively explore rich contextual information. Finally, by combining the core ideas of Siamese and Transformer, we present a simple and robust tracking framework called RPformer, which does not require any prior knowledge and avoids the trouble of adjusting hyperparameters. Numerous experiments show that the proposed tracking method achieves more outstanding performance than the state-of-the-art trackers on seven tracking benchmarks, which can meet the real-time requirements at a running speed exceeding 50.0 frames/s.

Recently, Siamese trackers have gained widespread attention in visual tracking due to their exceptional performance. However, many trackers still suffer from limitations in challenging scenarios, such as fast motion and scale variation, which hinder the full exploitation of target features. Consequently, the accuracy and efficiency of the trackers are limited. Therefore, this paper proposes an efficient attention network, called EAN, to improve tracking performance. The EAN comprises three primary components, namely a Transformer-s subnetwork, a Transformer-t subnetwork, and a Feature-Fused Attention Module (FFAM). The designed Transformer-s and Transformer-t subnetworks adopt complementary structures and functions to fully integrate and emphasize the relevant feature information, including channel and spatial features. The FFAM is responsible for fusing the multi-level features from both subnetworks, which establishes the global dependencies between the templates and search regions and enhances the discriminative power of the model. To further improve the tracking accuracy, a novel Feature-Aware Attention Module (FAAM) is introduced into the tracking prediction head to enhance the feature representation capability of the model. Finally, we propose an efficient EANTrack tracker based on EAN for robust tracking in complex scenarios, which exhibits significant advantages in challenging attributes. Experimental results on multiple benchmarks indicate that our approach achieves remarkable tracking performance with a real-time running speed of 55.6fps. Note to Practitioners —Siamese trackers have garnered considerable attention in the field of visual tracking due to their impressive performance. However, these trackers often face limitations in challenging scenarios, which impede the complete exploitation of target features. As a result, the accuracy and efficiency of many trackers are compromised. To address these issues, we propose an efficient tracker called EANTrack to enable robust tracking in complex scenarios. Our EANTrack exhibits significant advantages in handling challenging attributes. Please refer to our complete paper for detailed information on the EANTrack tracker and experimental results. Practitioners in the field can benefit from our research by leveraging our findings and methodologies in their work. We encourage further exploration and experimentation to enhance the performance and applicability of visual tracking systems.

Real-time acquisition of airport scene information is crucial for airport safety and optimization of airport utilization efficiency. However, detecting airport objects is still a challenging task due to the small size of person and vehicle targets in the airport scene images, insufficient public airport data, and so on, which makes it difficult to achieve high accuracy and real-time detection method in the airport scene simultaneously. This article proposes a novel airport object detection approach to address the challenge by integrating the advantages of improved YOLO, Swin Transformer, and attention mechanism (AD-YOLO). Specifically, we introduce the Swin Transformer, which retains the Transformer ability of global attention to obtain features and reduces the drawbacks of computational complexity, into the head net work based on YOLOv7 to improve the high-dimensional information feature fusion. We also design an effective channel spatial attention module (ECSA) and introduce a small object detection layer (SODL) to improve the detection accuracy of small targets in the airport scene. We test the proposed method on the self-constructed multiple airport surveillance dataset (MASD) containing 5736 images captured by actual airport and online airport video. The experimental results show that AD-YOLO achieves 71.6% mAP , exceeding the mAP of the baseline method by 4.4%. The proposed method has 101.4 FPS on the NVIDIA RTX3080 GPU and 17.8 FPS on the Jetson Orin NX, meeting the real-time and accuracy requirements of the airport scene. Finally, the experimental results on the public airport surface surveillance dataset (ASS) show that AD-YOLO outperform other detection methods, demonstrating the effectiveness.