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.
Highlights
Video surveillance systems are multistage computer vision systems capable of performing high end tasks [1]
The last video sequence, called Highway was taken from Changedetection project, which is a website that summarizes an academic benchmark for testing and ranking existing and new algorithms for change and motion detection, providing several datasets and tools [36]
In Video No 2, the traffic flow is from the bottom frame to the top frame, the set of this experiment is to evaluate the effectiveness of the vehicle counting process when the object is decreasing its relative size, the results demonstrate that our framework can address this scenario
Summary
Video surveillance systems are multistage computer vision systems capable of performing high end tasks [1]. Due to the increasing capabilities of hardware and software, the algorithms used to perform motion detection are getting better performance. There is still an increasing interest for developing new algorithms that are able to overtake limitations produced by human errors, since most of the systems cannot be checked automatically [2]. Video surveillance systems are broadly used in roads, banks, shops, schools, and other public places in order to protect social security [2,3]. The challenge for these video systems is to provide accuracy and confidence for detecting motion in any scenario. Many applications, for example traffic monitoring, are based on the unsupervised analysis of video sequences
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