DeTrack: In-model Latent Denoising Learning for Visual Object Tracking
Fudan University（复旦大学）；Shanghai Key Lab of Intelligent Information Processing（上海市智能信息处理重点实验室）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Shanghai Engineering Research Center of AI & Robotics（上海市人工智能与机器人工程技术研究中心）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）
Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets.
DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）
In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and…
ST-DETrack: Identity-Preserving Branch Tracking in Entangled Plant Canopies via Dual Spatiotemporal Evidence
Department of Biosciences, Durham University（生物科学系，达勒姆大学）；Department of Computer Science, Durham University（计算机科学系，达勒姆大学）；National Plant Phenomics Centre, IBERS, Aberystwyth University（国家植物表型中心，阿伯里斯特维斯大学）
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Bonn（波恩大学）；University of Stuttgart（斯图加特大学）；University of Tübingen（蒂宾根大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
DiffusionTrack: Diffusion Model For Multi-Object Tracking
Shenzen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Huazhong University of Science and Technology（华中科技大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge. Based on MOTR: End-to-End Multiple-Object Tracking with Transformer, we explore: 1) detect queries as anchors, 2) tracking as query denoising, 3) joint training on pseudo video clips generated from CrowdHuman dataset, and 4) using the YOLOX detection proposals for the anchor initialization of detect queries. Our method achieves 73.4% HOTA on the DanceTrack test set, surpassing the second-place solution by +6.8% HOTA.
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model
College of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
Seoul National University（首尔国立大学）；Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；NAVER Corp.（NAVER公司）
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context-aware scheme utilizing multiple expert auto-encoders; a context in our framework refers to the coarse category of the tracking target according to appearance patterns. In the pre-training phase, one expert auto-encoder is trained per category. In the tracking phase, the best expert auto-encode…
Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Utrecht University（乌得勒支大学）；University of New South Wales (UNSW)（新南威尔士大学）；NVIDIA Corporation（英伟达公司）；SURFsara
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising
Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University（上海交通大学医疗机器人研究所和自动化系）；Key Laboratory of System Control and Information Processing, Ministry of Education, Shang hai 200240, China（教育部系统控制与信息处理重点实验室，上海200240，中国）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
School of Computer Sciences and Technology, China University of Mining and Technology（中国矿业大学计算机科学与技术学院）；Mine Digitization Engineering Research Center of the Ministry of Education, China（教育部 mine 数字化工程研究中心）
Real-world imaging systems acquire measurements that are degraded by noise, optical aberrations, and other imperfections that make image processing for human viewing and higher-level perception tasks challenging. Conventional cameras address this problem by compartmentalizing imaging from high-level task processing. As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping and compression. This pipeline is optimized to obtain a visually pleasing image. High-level processing, on the …
Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting
Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI
Department of Radiation Oncology and Winship Cancer Institute, Emory University（放射肿瘤学系和Winship癌症研究所，埃默里大学）；Department of Radiology and Image Science and Winship Cancer Institute, Emory University（放射学系和影像科学以及Winship癌症研究所，埃默里大学）
