{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mat-cnn-sopc-motionless-analysis-of-traffic","title":"MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip","arxiv_id":"1807.02098","date":"2018-07-05","proceeding":null,"authors":["Somdip Dey","Grigorios Kalliatakis","Sangeet Saha","Amit Kumar Singh","Shoaib Ehsan","Klaus McDonald-Maier"],"abstract":"Intelligent Transportation Systems (ITS) have become an important pillar in\nmodern \"smart city\" framework which demands intelligent involvement of\nmachines. Traffic load recognition can be categorized as an important and\nchallenging issue for such systems. Recently, Convolutional Neural Network\n(CNN) models have drawn considerable amount of interest in many areas such as\nweather classification, human rights violation detection through images, due to\nits accurate prediction capabilities. This work tackles real-life traffic load\nrecognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin\nit as MAT-CNN- SOPC, which uses an intelligent re-training mechanism of the CNN\nwith known environments. The proposed methodology is capable of enhancing the\nefficacy of the approach by 2.44x in comparison to the state-of-art and proven\nthrough experimental analysis. We have also introduced a mathematical equation,\nwhich is capable of quantifying the suitability of using different CNN models\nover the other for a particular application based implementation.","url_abs":"http://arxiv.org/abs/1807.02098v2","url_pdf":"http://arxiv.org/pdf/1807.02098v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mat-cnn-sopc-motionless-analysis-of-traffic","repo_url":"https://github.com/somdipdey/MAT-CNN-SOPC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}