People counter on CCTV video using histogram of oriented gradient and Kalman filter methods

Adhinata, Faisal Dharma; Ikhsan, Muhammad; Wahyono, Wahyono

doi:10.14710/jtsiskom.2020.13660

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…A study involving CCTV in Indonesia was conducted by Adhinata et al (2020). In this experiment, CCTV can be a tracking tool and detect the number of people.…”

Section: Discussionmentioning

confidence: 99%

“…Object paths in several cameras are received by geometric projection to optimize the trajectory (Subudhi et al, 2019). Some researchers in Indonesia can build a system of analysis for detecting motion for tracking through CCTV (Adhinata et al, 2020 et al, 2020). Also, users get real-time motion detection information for human objects (Orisa et al, 2019).…”

Section: Big Data In Surveillance Purposesmentioning

confidence: 99%

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Utilization of Big Data Analysis Through Public Video, Virus Data Cooperation, and Social Media as the Surveillance to COVID-19 in Indonesia

Sirojjudin

2021

JSP

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This article discusses Big Data's use as a surveillance tool for the spread of Corona Virus Disease 2019 (COVID-19), both in Indonesia and the world. In Indonesia, the range of COVID-19 is increasingly sporadic, causing mass panic and Indonesia's geographical characteristics, which will be difficult when this spread could not control quickly. Researchers are conducting several studies to overcome this pandemic, including supervision, features, handling, mobility, patient interaction, treatment evaluation, and the biological structure. These studies become data and lead to Big Data. This article explores how to use Big Data analysis to monitor the spread of COVID-19 as a communication process that reflects mediated communication as a form of mobility and spatial relationships in communication practices. The method used in this article is a literature review and uses meta-synthesis techniques as its analysis. The literature sources used are articles in highly reputable international journals. Based on the reports, various ways to monitor the virus's spread, through public video data, GPS, and social media tracking, trace the patient's movement. Big Data can also provide data collaboration for viruses and pathogens for further research as digital mediated communication is anchored by the diversity of places and the mobility of people, data, and objects.

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“…A study involving CCTV in Indonesia was conducted by Adhinata et al (2020). In this experiment, CCTV can be a tracking tool and detect the number of people.…”

Section: Discussionmentioning

confidence: 99%

Section: Big Data In Surveillance Purposesmentioning

confidence: 99%

Utilization of Big Data Analysis Through Public Video, Virus Data Cooperation, and Social Media as the Surveillance to COVID-19 in Indonesia

Sirojjudin

2021

JSP

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“…Figure 5 shows the ratio of the center pixel to its neighbor. 2) Histogram of Oriented Gradients: There are four stages in performing feature extraction with this Histogram of Oriented Gradients, first doing a gradient calculation for the input image [25]. The feature extraction process with HOG is shown in Figure 7.…”

Section: Feature Extractionmentioning

confidence: 99%

A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Adhinata¹,

Ramadhan²,

Fauzi³

et al. 2023

JOIV : Int. J. Inform. Visualization

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Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies

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“…The F1 value on the 1000 and 5000 currencies is low because the resolution of the recording video is very influential. In Adhinata et al [24], video resolution also significantly affects the object detection results and speed. Decreasing the resolution results in fewer features being detected so that false negatives, which means positive data are recognized as negative by the system, often occur.…”

Section: A the Experiments Of Recording Resolutionmentioning

confidence: 99%

Real-time currency recognition on video using AKAZE algorithm

Adhinata

Adhitama

Segara

2021

J. Teknol. dan Sist. Komput

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Currency recognition is one of the essential things since everyone in any country must know money. Therefore, computer vision has been developed to recognize currency. One of the currency recognition uses the SIFT algorithm. The recognition results are very accurate, but the processing takes a considerable amount of time, making it impossible to run for real-time data such as video. AKAZE algorithm has been developed for real-time data processing because of its fast computation time to process video data frames. This study proposes the faster real-time currency recognition system on video using the AKAZE algorithm. The purpose of this study is to compare the SIFT and AKAZE algorithms related to a real-time video data processing to determine the value of F1 and its speed. Based on the experimental results, the AKAZE algorithm is resulting F1 value of 0.97, and the processing speed on each video frame is 0.251 seconds. Then at the same video resolution, the SIFT algorithm results in an F1 value of 0.65 and a speed of 0.305 seconds to process one frame. These results show that the AKAZE algorithm is faster and more accurate in processing video data.

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People counter on CCTV video using histogram of oriented gradient and Kalman filter methods

Cited by 6 publications

References 15 publications

Utilization of Big Data Analysis Through Public Video, Virus Data Cooperation, and Social Media as the Surveillance to COVID-19 in Indonesia

Utilization of Big Data Analysis Through Public Video, Virus Data Cooperation, and Social Media as the Surveillance to COVID-19 in Indonesia

A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Real-time currency recognition on video using AKAZE algorithm

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