Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

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Rosas-Arias, Leonel and Portillo-Portillo, Jose and Hernández-Suárez, Aldo and Olivares-Mercado, Jesus and Sánchez-Pérez, Gabriel and Toscano-Medina, Karina and Pérez-Meana, Hector and Sandoval Orozco, Ana Lucila and García Villalba, Luis Javier (2019) Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach. Sensors, 19 (13). p. 2848. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s19132848




Abstract

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.


Item Type:Article
Uncontrolled Keywords:video processing; motion detection; incremental learning; Incremental PCA; traffic flow
Subjects:Sciences > Computer science
Sciences > Computer science > Artificial intelligence
ID Code:67659
Deposited On:06 Sep 2021 14:58
Last Modified:06 Sep 2021 15:07

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