[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82198-en":3,"doc-seo-82198-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82198,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Equivariant Filter for High Performance Image Tracking Using an Event Camera","Image tracking estimates the transformation between a moving-scene image and a reference image, supporting autonomous control and robotics and broader computer vision tasks. This paper introduces an equivariant filter for planar image transformations using an event camera. It leverages the Asynchronous Event Blob (AEB) tracker for feature-position measurements, then applies an equivariant filter on SE(2) symmetry. An equivalent-measurement update reduces temporal correlation. Experiments on rotational-motion datasets show smooth tracking up to 7000 pixels per second and improved robustness versus direct optimisation and covariance intersection.","arXiv :2607 .09103v1 [ cs .CV] 10 Jul 2026  \n\n| EQUIVARIANT FILTER FOR HIGH PERFORMANCE IMAGE TRACKING\u003Cbr>USING AN EVENT CAMERA |  |  |\n| --- | --- | --- |\n| AUTHOR ACCEPTED VERSION⋆ |  |  |\n| Angus Apps\u003Cbr>Systems Theory and Robotics Group School of Engineering Australian National University Canberra, Australia [Angus.Apps@anu.edu.au](Angus.Apps@anu.edu.au) | Yixiao Ge\u003Cbr>Systems Theory and Robotics Group School of Engineering Australian National University Canberra, Australia [Yixiao.Ge@anu.edu.au](Yixiao.Ge@anu.edu.au) | Timothy L. Molloy\u003Cbr>School of Engineering Australian National University Canberra, Australia [Timothy.Molloy@anu.edu.au](Timothy.Molloy@anu.edu.au) |\n| Robert Mahony\u003Cbr>Systems Theory and Robotics Group\u003Cbr>School of Engineering\u003Cbr>Australian National University\u003Cbr>Canberra, Australia\u003Cbr>[Robert.Mahony@anu.edu.au](Robert.Mahony@anu.edu.au)\u003Cbr>July 13, 2026\u003Cbr>ABSTRACT\u003Cbr>Image tracking is the problem of estimating the transformation that relates a moving image of a scene to an original reference image. The problem is important in control of autonomous vehicles or robots, where the image encodes information about the motion of the camera or environment, as well as in pure computer vision applications. In this paper, we present an equivariant filter design for high performance tracking of planar image transformations using an event camera. The design exploits the Asynchronous Event Blob (AEB) tracker [1] to extract feature-position measurements from the raw event stream, and an equivariant filter to compute an affine image translation and rotation using the special Euclidean group symmetry. The equivariant filter incorporates an equivalent-measurement update step that de-correlates the (highly temporally correlated) feature-position measurements provided by the AEB tracker. We evaluate the design experimentally using two datasets involving general and fast rotational motion. We benchmark results against direct optimisation (estimating the relative transformation from the raw blob tracks), and a covariance intersection approach for overcoming data correlation. Our design provides smooth image tracking for features moving up to 7000 pixels per second on the image plane.\u003Cbr>1 Introduction\u003Cbr>Image tracking is the problem of estimating the transformation that relates a moving image of a scene to an original reference image. Image tracking is widely used in many computer vision applications, such as perspective correction [2] and imagestabilisation [3], and has proven valuable in many robotics applications, such as visual odometry [4] and vision-based control [5, 6] . Classical computer vision techniques exploit the algebraic relationship between sets of matching feature points to compute the corresponding image transformation [7] . With sufficient matching points, algebraic algorithms can compute quality estimates of the transformation between consecutive image frames. However, these estimates are computed independently for each pair of images and do not exploit the temporal evolution of the image transformation caused by real-world camera motion. To address this issue, authors in the systems and control community have developed non-linear observers for image transformation estimation. In the last decade, deterministic observers for homography tracking were developed [8, 9] that provide robust performance with strong stability guarantees. Recent years have seen the development of stochastic filters for homography tracking. [10] proposed an interacting multiple model filter that runs parallel iterated extended Kalman filters with different process noise to manage model mismatch. [11] proposed an equivariant filter to estimate homography and structure |  |  |\n\n⋆ ©2026 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND. This work was supported by the Australian Research Council under the Discovery Project DP250100112 . Accepted for presentation at IFAC World","cbCailOmmtcOWE82","https://ap.wps.com/l/cbCailOmmtcOWE82","pdf",4315697,2,1,9,"English","en",105,"# Introduction\n## Problem motivation and related work\n## Event camera feature tracking\n## Correlated measurements and filtering approach\n## Proposed equivariant filter design\n## Experimental evaluation and benchmarks","[{\"question\":\"What transformation does the proposed method estimate for image tracking?\",\"answer\":\"It estimates planar affine image translation and rotation between a moving image and a reference image, using the special Euclidean group symmetry structure in the filter design.\"},{\"question\":\"How are feature-position measurements obtained from event data?\",\"answer\":\"The method uses the Asynchronous Event Blob (AEB) tracker to extract feature-position measurements from the raw event stream.\"},{\"question\":\"Why is an additional update step needed in the equivariant filter?\",\"answer\":\"The AEB-provided feature-position measurements are highly temporally correlated, so an equivalent-measurement update de-correlates them before applying the tracking filter.\"}]",1784178760,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"equivariant-filter-for-high-performance-image-tracking-using-an-event-camera","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/equivariant-filter-for-high-performance-image-tracking-using-an-event-camera/82198/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What transformation does the proposed method estimate for image tracking?","Question",{"text":75,"@type":76},"It estimates planar affine image translation and rotation between a moving image and a reference image, using the special Euclidean group symmetry structure in the filter design.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are feature-position measurements obtained from event data?",{"text":80,"@type":76},"The method uses the Asynchronous Event Blob (AEB) tracker to extract feature-position measurements from the raw event stream.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is an additional update step needed in the equivariant filter?",{"text":84,"@type":76},"The AEB-provided feature-position measurements are highly temporally correlated, so an equivalent-measurement update de-correlates them before applying the tracking filter.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]