{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:55Z","timestamp":1740202075115,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Vehicle detection and recognition is the research focus in Intelligent Transportation System (ITS) with many challenges. Based on the success of Convolutional neural networks (CNN) in object detection and image classification, we propose a ZLCC to locate vehicle and classify its maker &amp;amp; model &amp;amp; shape. Our framework focuses on two new perspectives: (i) how to generate a small number of high quality region proposals, (ii) how to improve vehicle classification accuracy rate by hierarchical learning policy. We use deep network's responses to generate aware-map in detection, and train network with multiple candidates softmax regression. We demonstrate the success of ZLCC on Stanford Cars for using the deep VGG16 architecture.<\/jats:p>","DOI":"10.3233\/978-1-61499-785-6-350","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:24Z","timestamp":1740133644000},"source":"Crossref","is-referenced-by-count":0,"title":["ZLCC: Vehicle Detection and Fine-Grained Classification Based on Deep Network Responses and Hierarchical Learning"],"prefix":"10.3233","author":[{"family":"Joya Chen","sequence":"additional","affiliation":[]},{"family":"Li Shunxi","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Technology and Intelligent Transportation Systems"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:13:46Z","timestamp":1740136426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-784-9&spage=350&doi=10.3233\/978-1-61499-785-6-350"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-785-6-350","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}