BACTrack: Building Appearance Collection for Aerial Tracking
Beijing Institute of Technology（北京理工大学）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（教育部光电成像技术与系统重点实验室）；Chongqing Innovation Center, Beijing Institute of Technology（北京理工大学重庆创新中心）
Siamese network-based trackers have shown remarkable success in aerial tracking. Most previous works, however, usually perform template matching only between the initial template and the search region and thus fail to deal with rapidly changing targets that often appear in aerial tracking. As a remedy, this work presents Building Appearance Collection Tracking (BACTrack). This simple yet effective tracking framework builds a dynamic collection of target templates online and performs efficient multi-template matching to achieve robust tracking. Specifically, BACTrack mainly comprises a Mixed-Temporal Transformer (MTT) and an appearance discriminator. The former is responsible for efficiently building relationships between the search region and multiple target templates in parallel through a mixed-temporal attention mechanism. At the same time, the appearance discriminator employs an online adaptive template-update strategy to ensure that the collected multiple templates remain reliable and diverse, allowing them to closely follow rapid changes in the target's appearance and suppress background interference during tracking. Extensive experiments show that our BACTrack achieves top performance on four challenging aerial tracking benchmarks while maintaining an impressive speed of over 87 FPS on a single GPU. Speed tests on embedded platforms also validate our potential suitability for deployment on UAV platforms.
Beyond Appearance: A Multi-cue Framework and Large-scale Benchmark for Pedestrian Association and Tracking on Mobile Aerial-Ground Platforms
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）；Ningbo Institute, Northwestern Polytechnical University（西北工业大学宁波研究院）；National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（国家空天地海一体化大数据应用技术工程实验室）；Shenzhen University of Advanced Technology（深圳先进技术研究院）
TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices
Instituto de Telecomunicações（电信研究所）；University of Beira Interior（贝拉内格罗大学）；India Statistical Institute（印度统计研究所）；JSS Science and Technology University（JSS科技大学）
A Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles
School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）；School of Computer Science and Electronic Engineering and the Centre for Vision, Speech and Signal Processing, University of Surrey（萨里大学计算机科学与电子工程学院及视觉、语音与信号处理中心）
3D Gaussian Splatting for Fine-Detailed Surface Reconstruction in Large-Scale Scene
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University（土地测量与地理信息系，香港理工大学）；School of Geodesy and Geomatics, Wuhan University（测绘学院，武汉大学）
WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses
Center for AI Research, VinUniversity（VinUniversity人工智能研究中心）；College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；VinRobotics（VinRobotics公司）
T-SiamTPN: Temporal Siamese Transformer Pyramid Networks for Robust and Efficient UAV Tracking
Department of Electrical Engineering, Amirkabir University of Technology (AUT)（电气工程系，阿米尔卡比尔技术大学）；Qatar Computing Research Institute Hamad Bin Khalifa University（卡塔尔计算研究所，哈马德·本·卡西姆大学）
