Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters. However, predefined parameters introduce much effort in tuning them and they still fail to adapt to new situations that the designer did not think of. In this work, a novel approach is proposed to online automatically and adaptively learn spatio-temporal regularization term. Spatially local response map variation is introduced as spatial regularization to make DCF focus on the learning of trust-worthy parts of the object, and global response map variation determines the updating rate of the filter. Extensive experiments on four UAV benchmarks have proven the superiority of our method compared to the state-of-the-art CPU- and GPU-based trackers, with a speed of ~60 frames per second running on a single CPU.
Our tracker is additionally proposed to be applied in UAV localization. Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method. The code is available at https://github.com/vision4robotics/AutoTrack.
STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Optics and Electronics（光电技术研究所）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Wuhan University（武汉大学）；University of Arkansas（阿肯色大学）；Leador Spatial Information Technology Corporation（立得空间信息技术股份有限公司）
In a complex urban scene, observation from a single sensor unavoidably leads to voids in observations, failing to describe urban objects in a comprehensive manner. In this paper, we propose a spatio-temporal-spectral-angular observation model to integrate observations from UAV and mobile mapping vehicle platform, realizing a joint, coordinated observation operation from both air and ground. We develop a multi-source remote sensing data acquisition system to effectively acquire multi-angle data of complex urban scenes. Multi-source data fusion solves the missing data problem caused by occlusion…
Efficient On-Board Processing of Oblique UAV Video for Rapid Flood Extent Mapping
IDLab, Department of Information Technology, Ghent University - imec（IDLab，信息科技系，根特大学 - imec）；Environmental Intelligence Unit, VITO (Flemish Institute for Technological Research)（环境智能单元，VITO（佛兰德斯技术研究院））
Tongji University（同济大学）；Hong Kong Polytechnic University（香港理工大学）
Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years. However, existing algorithms still face difficulties such as partial occlusion, clutter background, and other challenging visual factors. Inspired by the cutting-edge attention mechanisms, a novel object tracking framework is proposed to leverage multi-level visual attention. Three primary attention, i.e., contextual attention, dimensional attention, and spatiotemporal attention, are integrated into the training and detection stages of correlation filter-based tracking pipeline. Therefore, the prop…
Korea University（高丽大学）；Hallym University（翰林大学）；Ajou University（亚洲大学）；UC-Irvine（加州大学尔湾分校）
Autonomous surveillance unmanned aerial vehicles (UAVs) are deployed to observe the streets of the city for any suspicious activities. This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets. An extensive database is collected from UAV surveillance drones. With the aid of artificial intelligence (AI), fire stations can swiftly identify the presence of a fire emerging in the neighborhood. Spatio-temporal split learning is applied to this scenario to preserve privacy and globally train a fire classification model. Fires are hazardous natural disas…
Texas A&M University（德克萨斯农工大学）；New York University（纽约大学）；Wormpex AI Research（Wormpex人工智能研究院）；UT Austin（德克萨斯大学奥斯汀分校）
Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solu…
FTDMamba: Frequency-Assisted Temporal Dilation Mamba for Unmanned Aerial Vehicle Video Anomaly Detection
Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）计算机科学与工程系）
Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant challenges for inference in terms of storage, communication and computation. Compressive cameras have emerged as a potential solution to deal with the data deluge issues in such applications. However, inference tasks such as action recognition require high quality features which implies reconstructing the original video data. Much work in compressive sensing (CS) theory is geared towards solving the reconstruction problem, where state-of-the-…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；University of Campinas（坎皮纳斯大学）；Universidade Estadual Paulista（圣保罗州立大学）；University of Stirling（斯特灵大学）
Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a…
Wuhan University（武汉大学）；Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）
Unmanned aerial vehicles (UAVs) are now widely applied to data acquisition due to its low cost and fast mobility. With the increasing volume of aerial videos, the demand for automatically parsing these videos is surging. To achieve this, current researches mainly focus on extracting a holistic feature with convolutions along both spatial and temporal dimensions. However, these methods are limited by small temporal receptive fields and cannot adequately capture long-term temporal dependencies which are important for describing complicated dynamics. In this paper, we propose a novel deep neural …
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）
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所（IAIS））；University of Bonn（波恩大学）；Institute for Sugar Beet Research (IfZ)（甜菜研究所（IfZ））；Technical University of Munich（慕尼黑工业大学）
UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated…
Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers
Department of Computing, Federal University of São Carlos (UFSCar)（计算机系，萨o卡罗斯联邦大学）；Center for Research on Biodiversity Dynamics and Climate Change and Department of Biodiversity, Bioscience Institute, São Paulo State University (UNESP)（生物多样性动态与气候变化研究中心和生物多样性系，圣保罗州立大学）；Brazilian Agricultural Research Corporation (EMBRAPA)（巴西农业研究 corporation）；Wageningen University & Research (WUR)（瓦赫宁根大学与研究）
FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；College of Soil and Water Conservation, Southwest Forestry University（西南林业大学水土保持学院）；College of Civil Engineering, Southwest Forestry University（西南林业大学土木工程学院）
