Traffic Analytics With Low-Frame-Rate Videos

• Published in: IEEE transactions on circuits and systems for video technology Vol. 28; no. 4; pp. 878 - 891
• Main Authors: Luo, Zhiming, Jodoin, Pierre-Marc, Su, Song-Zhi, Li, Shao-Zi, Larochelle, Hugo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Automobiles; Cameras; Convolutional neural network (CNN); Driving conditions; Image classification; Image segmentation; Motion perception; Neural networks; Roads; Traffic; traffic density; Traffic models; Traffic volume; transfer learning; video surveillance; Videos

In this paper, we investigate the possibility of monitoring highway traffic based on videos whose frame rate is too low to accurately estimate motion features. The goal of the proposed method is to recognize traffic conditions instead of measuring them, as is usually the case. The main advantage of...