{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T04:48:15Z","timestamp":1773550095710,"version":"3.50.1"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62102387 and 62236002"],"award-info":[{"award-number":["62102387 and 62236002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Research Foundation of Department of Education of Anhui Province","award":["2025AHGXZK31170"],"award-info":[{"award-number":["2025AHGXZK31170"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Event cameras with high dynamic range and temporal resolution, which are bio-inspired vision sensors, have shown great potential in event-based tracking tasks, particularly in scenarios involving rapid motion and low levels of illumination. Nonetheless, the efficient extraction of sparse information from event camera remains a persistent challenge. Meanwhile, the event camera works asynchronously, generating a continuous stream of events, rendering it highly compatible with\n                    <jats:bold>Spiking Neural Networks (SNNs)<\/jats:bold>\n                    due to their event-driven nature and low power consumption. Motivated by the issues mentioned above, we propose an\n                    <jats:bold>Efficient Hybrid Cascade Tracker<\/jats:bold>\n                    (\n                    <jats:bold>EHCT<\/jats:bold>\n                    ) with SNN for object tracking in the event domain. We combine the transformer, convolutional network, and SNN structure skillfully to form the basic\n                    <jats:bold>Hybrid CNN-SNN-Transformer (HCST)<\/jats:bold>\n                    block structure, which is also the central component part of our EHCT network. The HCST block is primarily utilized to process the incoming event data and extract information from both local and global contexts. After several cascade HCST blocks, these two types of information will be efficiently integrated with the preprocessed raw data, which are then fed into the Classifier and Regressor head to produce the bounding box (bbox) of the tracked target. Extensive experiments on various event and RGB frame-based datasets demonstrated that our proposed EHCT algorithm outperforms most of the existing state-of-the-art trackers by a significant margin and also achieves a great advantage in terms of energy consumption.\n                    <jats:italic toggle=\"yes\">\n                      Our source code will be available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/masac11\/EHCT\">https:\/\/github.com\/masac11\/EHCT<\/jats:ext-link>\n                    <\/jats:italic>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3795518","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T14:11:55Z","timestamp":1770127915000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Hybrid Cascade Tracker with Spiking Neural Networks for Event Domain Tracking"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3594-2151","authenticated-orcid":false,"given":"Yun","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei, China, Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China, and Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5010-7954","authenticated-orcid":false,"given":"Hongfu","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei, China, Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, China, and Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3005-3405","authenticated-orcid":false,"given":"Chunyu","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei, China, Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, China, and Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2337-0664","authenticated-orcid":false,"given":"Qiaoyun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei, China, Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, China, and Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2367-7167","authenticated-orcid":false,"given":"Changyin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei, China, Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, China, and Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5461-3986","authenticated-orcid":false,"given":"Richang","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00628"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_13"},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Bu Tong","year":"2022","unstructured":"Tong Bu, Wei Fang, Jianhao Ding, P. 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