The design and implementation of a vision-based people counting system in buses

Perng, Jau-Woei; Wang, Ting-Yen; Hsu, Ya-Wen; Wu, Bing-Fei

doi:10.1109/icsse.2016.7551620

Cited by 28 publications

(8 citation statements)

References 6 publications

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“…In pre-processing, morphological operators including erosion and dilation are typically used by mainstream researchers to detect the required blob. For noise removing Gaussian and median filter methods have been used as revealed in Table I Connected component labeling method for blob extraction is used by [2], [20], [18] and [13] shown in Fig. 4.…”

Section: A Blob Based Techniquesmentioning

confidence: 99%

“…In first Row researcher [16], [9] considered Head as ROI. In the second row some [27], [25], [23] & [18] considered Head-shoulders as ROI. Some of them are discussed in this paper [1], [27], [21]& [28] also considered whole overhead body as ROI.…”

Section: A Blob Based Techniquesmentioning

confidence: 99%

“…9. The First Row shows Head as ROI [16], [9] the second row whose Head-shoulder [27], [25], [23] & [18] as ROI while the third in third row [1] , [27], [21]& [28]whole overhead body has been considered as ROI, respectively.…”

Section: A Blob Based Techniquesmentioning

confidence: 99%

“…Table I demonstrate that most of the researchers except [12], [16], [22], [22], [8] & [19] used overhead background subtraction methods for people counting. Authors in [25], [29], [28] & [18] have taken account of virtual lines for counting people in top-view images as shown in Fig. 10 below.…”

Section: A Blob Based Techniquesmentioning

confidence: 99%

See 3 more Smart Citations

Person Detection from Overhead View: A Survey

Ahmad¹,

Ahmed²,

Ullah³

et al. 2019

IJACSA

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In recent years, overhead view based person detection gained importance, due to handling occlusion problem and providing better coverage in scene, as compared to frontal view. In computer vision, overhead based person detection holds significant importance in many applications including person detection, person counting, person tracking, behavior analysis and occlusion free surveillance system, etc. This paper aims to provide a comprehensive survey on recent development and challenges related to person detection from top view. To the best of our knowledge, it is the first attempt which provides the survey of different overhead person detection techniques. This paper provides an overview of state of the art overhead based person detection methods and guidelines to choose the appropriate method in real life applications. The techniques are divided into two main categories: the blob-based techniques and the feature-based techniques. Various detection factors such as field of view, region of interest, color space, image resolution are also examined along with a variety of top view datasets.

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Section: A Blob Based Techniquesmentioning

confidence: 99%

Section: A Blob Based Techniquesmentioning

confidence: 99%

Section: A Blob Based Techniquesmentioning

confidence: 99%

Section: A Blob Based Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Person Detection from Overhead View: A Survey

Ahmad¹,

Ahmed²,

Ullah³

et al. 2019

IJACSA

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show abstract

“…A non-linear function maps the feature vectors and people count. The method adopted by Perng et al [10], estimates the people count using a single camera that fixed overhead. The foreground was extracted using background subtraction so that only the moving objects were then processed.…”

Section: Literature Surveymentioning

confidence: 99%

Vision Based Algorithm for People Counting Using Deep Learning

Padmashini¹,

Manjusha²,

Parameswaran³

2018

IJET

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Estimating the number of people in a particular scene has always been an important topic of research in computer vision and digital image processing. People counting has wide applications in scenario ranging from analyzing the customer's choice and improving the quality of service in retail stores, supermarkets and shopping malls to managing human resources and optimizing the energy usage in office buildings. While there exists algorithms for counting people in a scene, some algorithm have set their benchmark in performance with respect to efficiency, flexibility and accuracy. In this paper, an attempt has been made to perform people counting using Deep Neural Networks (DNN) on comparison with existing image processing based algorithms like Histogram of Oriented Gradients with Support Vector Machine (HoG with SVM), Local Binary Pattern (LBP) based Adaboost classifier and contour based people detection. The proposed DNN based approach has higher accuracy at 90% and less false negatives.

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Design of an Image Skeletonization Based Algorithm for Overcrowd Detection in Smart Building

Manjusha

Parameswaran

2019

Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

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The design and implementation of a vision-based people counting system in buses

Cited by 28 publications

References 6 publications

Person Detection from Overhead View: A Survey

Person Detection from Overhead View: A Survey

Vision Based Algorithm for People Counting Using Deep Learning

Design of an Image Skeletonization Based Algorithm for Overcrowd Detection in Smart Building

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