Design of Person Flow Counting and Monitoring System Based on Feature Point Extraction of Optical Flow

Wu, Xia

doi:10.1109/isdea.2014.92

Cited by 4 publications

(3 citation statements)

References 4 publications

(3 reference statements)

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“…When using a robust and time-efficient region proposal method such as selective search algorithm [13], the accuracy and computational complexity of the detector is mainly given by a used object recognition method. In colour images, the recognition of persons typically relies on optical flow features [11,17,18]. An alternative approach to the detection of persons is the detection of their heads and shoulders [19]; however, a head itself can provide a strong feature due to its almost circular shape.…”

Section: Introductionmentioning

confidence: 99%

Person Detection for an Orthogonally Placed Monocular Camera

Škrabánek

Doležel

Němec

et al. 2020

Journal of Advanced Transportation

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Counting of passengers entering and exiting means of transport is one of the basic functionalities of passenger flow monitoring systems. Exact numbers of passengers are important in areas such as public transport surveillance, passenger flow prediction, transport planning, and transport vehicle load monitoring. To allow mass utilization of passenger flow monitoring systems, their cost must be low. As the overall price is mainly given by prices of the used sensor and processing unit, we propose the utilization of a visible spectrum camera and data processing algorithms of low time complexity to ensure a low price of the final product. To guarantee the anonymity of passengers, we suggest orthogonal scanning of a scene. As the precision of the counting is relevantly influenced by the precision of passenger recognition, we focus on the development of an appropriate recognition method. We present two opposite approaches which can be used for the passenger recognition in means of transport with and without entrance steps, or with split level flooring. The first approach is the utilization of an appropriate convolutional neural network (ConvNet), which is currently the prevailing approach in computer vision. The second approach is the utilization of histograms of oriented gradients (HOG) features in combination with a support vector machine classifier. This approach is a representative of classical methods. We study both approaches in terms of practical applications, where real-time processing of data is one of the basic assumptions. Specifically, we examine classification performance and time complexity of the approaches for various topologies and settings, respectively. For this purpose, we form and make publicly available a large-scale, class-balanced dataset of labelled RGB images. We demonstrate that, compared to ConvNets, the HOG-based passenger recognition is more suitable for practical applications. For an appropriate setting, it defeats the ConvNets in terms of time complexity while keeping excellent classification performance. To allow verification of theoretical findings, we construct an engineering prototype of the system.

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Section: Introductionmentioning

confidence: 99%

Person Detection for an Orthogonally Placed Monocular Camera

Škrabánek

Doležel

Němec

et al. 2020

Journal of Advanced Transportation

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“…Different ways of image and video processing can be considered in general. Methods based on shape feature, model learning, or area estimation are widely used [Wu 2014].…”

Section: Detection Based On Image Processingmentioning

confidence: 99%

Development of Image Procesing System for Person Detection

Štursa¹,

Honc²,

Doležel³

2020

MM SJ

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With the rise of modern technology in computer science and engineering, as well as with the growing population in big cities around the world, many new approaches for person detection have become a very interesting and demanding topic. Person detection is a necessary building block for people monitoring systems and, therefore, various detection methods must be inspected comprehensively in order to select the one with the most suitable performance and accuracy. In this paper, a set of different image processing techniques applied to images captured from a high angle were used for people detection. To be more specific, selected feature extraction techniques, like edge detectors, local binary patterns, pixel intensities or histograms of oriented gradients, were used in combination with several classification algorithms. The combinations of each feature extractor and its best classifier were selected for performance comparison. As a result of the comparison, the most suitable image processing method for person detection in high angle image is presented at the end of the paper.

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“…Generally, several ways of image and video processing can be considered as tools for person flow detection. In particular, statistical methods based on model learning, shape feature, skin color feature or area estimation are widely used [27].…”

Section: Introductionmentioning

confidence: 99%

On Possibilities of Human Head Detection for Person Flow Monitoring System

Doležel

Štursa

Škrabánek

2019

Advances in Computational Intelligence

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Along with the development of human society, economy, industry and engineering, as well as with growing population in the world's biggest cities, various approaches to person detection have become the subject of great interest. One approach to developing a person detection system is proposed in this paper. A high-angle video sequence is considered as the input to the system. Then, three classification algorithms are considered: support vector machines, pattern recognition neural networks and convolutional neural networks. The results showed very little difference between the classifiers, with the overall accuracy more than 95 % over a testing set.

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Design of Person Flow Counting and Monitoring System Based on Feature Point Extraction of Optical Flow

Cited by 4 publications

References 4 publications

Person Detection for an Orthogonally Placed Monocular Camera

Person Detection for an Orthogonally Placed Monocular Camera

Development of Image Procesing System for Person Detection

On Possibilities of Human Head Detection for Person Flow Monitoring System

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