# CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras arXiv: https://arxiv.org/abs/2401.02826 · HTML: https://mlsanity.com/paper/2401.02826/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 43 of its 72 references that are in the map. - First posted on arXiv 5 Jan 2024. ## Builds on (most cited first, 30 of 43) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019) - [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019) - [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018) - [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021) - [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021) - [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019) - [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022) - [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021) - [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018) - [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022) - [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020) - [Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking](https://mlsanity.com/paper/2103.11681/) (2021) - [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015) - [Siamese Box Adaptive Network for Visual Tracking](https://mlsanity.com/paper/2003.06761/) (2020) - [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019) - [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019) - [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021) - [Visual Prompt Multi-Modal Tracking](https://mlsanity.com/paper/2303.10826/) (2023) - [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017) - [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020) - [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021) - [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021) - [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018) - [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018) - [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022) - [Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1811.08230/) (2018) - [Generalized Relation Modeling for Transformer Tracking](https://mlsanity.com/paper/2303.16580/) (2023) - [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021) - [Provable Dynamic Fusion for Low-Quality Multimodal Data](https://mlsanity.com/paper/2306.02050/) (2023) ## Built on by (most cited first, 17 of 17) - [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024) - [Adaptive Perception for Unified Visual Multi-modal Object Tracking](https://mlsanity.com/paper/2502.06583/) (2025) - [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025) - [Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset](https://mlsanity.com/paper/2403.05839/) (2024) - [Awesome Multi-modal Object Tracking](https://mlsanity.com/paper/2405.14200/) (2024) - [MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice](https://mlsanity.com/paper/2409.03358/) (2024) - [When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework](https://mlsanity.com/paper/2507.13659/) (2025) - [ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation](https://mlsanity.com/paper/2412.09105/) (2024) - [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025) - [Rethinking Event-Based Object Detection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning](https://mlsanity.com/paper/2605.08825/) (2026) - [M2E-UAV: A Benchmark and Analysis for Onboard Motion-on-Motion Event-Based Tiny UAV Detection](https://mlsanity.com/paper/2605.10496/) (2026) - [Sparse-Dense Mixture of Experts Adapter for Multi-Modal Tracking](https://mlsanity.com/paper/2603.13719/) (2026) - [PFM-VEPAR: Prompting Foundation Models for RGB-Event Camera based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2603.19565/) (2026) - [Learning Progressive Adaptation for Multi-Modal Tracking](https://mlsanity.com/paper/2603.21100/) (2026) - [Spatial Orthogonal Refinement for Robust RGB-Event Visual Object Tracking](https://mlsanity.com/paper/2603.27913/) (2026) - [E-TraMamba: A New Paradigm for Efficient Long-Term 3D Feature Tracking with Event Cameras](https://mlsanity.com/paper/2607.02866/) (2026) - [SkyEV: RGB-Event UAV detection and tracking dataset and baseline](https://mlsanity.com/paper/2607.18747/) (2026) ## Abstract (arXiv) Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution (346 × 260) is low for practical applications. Actually, only visible cameras are deployed in many practical systems, and the newly designed neuromorphic cameras may have different resolutions. The latest neuromorphic sensors can output high-definition event streams, but it is very difficult to achieve strict alignment between events and frames on both spatial and temporal views. Therefore, how to achieve accurate tracking with unaligned neuromorphic and visible sensors is a valuable but unresearched problem. In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras. We build the first unaligned frame-event dataset CRSOT collected with a specially built data acquisition system, which contains 1,030 high-definition RGB-Event video pairs, 304,974 video frames. In addition, we propose a novel unaligned object tracking framework that can realize robust tracking even using the loosely aligned RGB-Event data. Specifically, we extract the template and search regions of RGB and Event data and feed them into a unified ViT backbone for feature embedding. Then, we propose uncertainty perception modules to encode the RGB and Event features, respectively, then, we propose a modality uncertainty fusion module to aggregate the two modalities. These three branches are jointly optimized in the training phase. Extensive experiments demonstrate that our tracker can collaborate the dual modalities for high-performance tracking even without strictly temporal and spatial alignment. The source code, dataset, and pre-trained models will be released at https://github.com/Event-AHU/CrossResolutionSOT.