Abstract
Counting the number of pedestrians in urban environments has become an area of interest over the past few years. Its applications include studies to control vehicular traffic lights, urban planning, market studies, and detection of abnormal behaviors. However, these tasks require the use of intelligent algorithms of high computational demand that need to be trained in the environment being studied. This article presents a novel method to estimate pedestrian flow in uncontrolled environments by using the fractal dimension measured through the box-counting algorithm, which does not require the use of image pre-processing and intelligent algorithms. Four scenarios were used to validate the method presented in this article, of which the last scene was a low-light surveillance video, showing experimental results with a mean relative error of 4.92% when counting pedestrians. After comparing the results with other techniques that depend on intelligent algorithms, we can confirm that this method achieves improved performance in the estimation of pedestrian traffic.
Highlights
The data obtained through pedestrian counting can be, in turn, directly applied to pedestrian flow detection, surveillance systems for tracking and detection of inappropriate behavior in low-density areas, urban planning, marketing studies in shopping malls, and critical area analysis in evacuations [7, 8]
We develop a novel method to estimate pedestrian count that is based on calculating the fractal dimension of images in structured or unstructured scenes, by using segmentation algorithms
This article presents a novel method to estimate the number of pedestrians, based on the differential box-counting method (DBC) to measure the fractal dimension in videos
Summary
When Video Analytics’ algorithms are integrated with video, they are referred to as Video Software Systems [1]. These systems perform tasks that use statistical techniques and machine learning to extract and identify individual persons, track them, and compute the number of pedestrians automatically in high-traffic areas, with direct applications in real-time processing systems [2, 3, 4, 5, 6].
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