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Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (\n                    <jats:italic toggle=\"yes\">LWVDT<\/jats:italic>\n                    ) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The\n                    <jats:italic toggle=\"yes\">LWVDT<\/jats:italic>\n                    is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The\n                    <jats:italic toggle=\"yes\">LWVDT<\/jats:italic>\n                    processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting\/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.\n                  <\/jats:p>","DOI":"10.3233\/kes-230062","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T12:10:46Z","timestamp":1704802246000},"page":"335-357","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced real-time road-vehicles\u2019 detection and tracking for driving assistance"],"prefix":"10.1177","volume":"28","author":[{"given":"Wael","family":"Farag","sequence":"first","affiliation":[{"name":"American University of the Middle East","place":["Kuwait"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Nadeem","sequence":"additional","affiliation":[{"name":"American University of the Middle East","place":["Kuwait"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,5,1]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDT-180064"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.2174\/2666255813999200727163102"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJCSE.2021.118100"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","unstructured":"FaragW. Bayesian localization in real-time using probabilistic maps and unscented-Kalman-filters. J Eng Res [Internet]. 2021. Available from: doi: 10.36909\/jer.11073.","DOI":"10.36909\/jer.11073"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.3233\/jifs-190634"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.2174\/2213275912666190126095547"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"FaragW SalehZ. An advanced road-lanes finding scheme for self-driving cars. In: 2nd Smart Cities Symposium (SCS 2019). Institution of Engineering and Technology; 2019.","DOI":"10.1049\/cp.2019.0221"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","unstructured":"FaragW. Multiple road-objects detection and tracking for autonomous driving. J Eng Res [Internet]. 2021. 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