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To the best of our knowledge, there is a lack of holistic frameworks for estimating traffic density from traffic surveillance camera data with 4\u00a0L characteristics. Therefore, we propose a framework for estimating traffic density using uncalibrated traffic surveillance cameras. The proposed framework consists of two major components: camera calibration and vehicle detection. The camera calibration method estimates the actual length between pixels in the images and videos, and the vehicle counts are extracted from the deep-learning-based vehicle detection method. Combining the two components, high-granular traffic density can be estimated. To validate the proposed framework, two case studies were conducted in Hong Kong and Sacramento. The results show that the Mean Absolute Error (MAE) for the estimated traffic density is 9.04 veh\/km\/lane in Hong Kong and 7.03 veh\/km\/lane in Sacramento. The research outcomes can provide accurate traffic density without installing additional sensors.<\/jats:p>","DOI":"10.1007\/s40747-023-01117-0","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T06:05:34Z","timestamp":1687413934000},"page":"7171-7195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Turning traffic surveillance cameras into intelligent sensors for traffic density estimation"],"prefix":"10.1007","volume":"9","author":[{"given":"Zijian","family":"Hu","sequence":"first","affiliation":[]},{"given":"William H. K.","family":"Lam","sequence":"additional","affiliation":[]},{"given":"S. C.","family":"Wong","sequence":"additional","affiliation":[]},{"given":"Andy H. 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