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    BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV
    (2024-01-01)
    Kujareanpaisal, Poonnaphop
    ;
    Mayhasap, Rujira
    ;
    Tea-Makorn, Pin Pin
    ;
    Jindahra, Pavitra
    ;
    Starita, Stefano
    The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions.
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    Exploring the i3DVAE-LSTM Framework for Generating Exceptionally Rare Anomaly Signals
    (2023-01-01)
    Kaewkiriya, Thongchai
    ;
    Woraratpanya, Kuntpong
    In the era of data-driven approaches, ensuring data quality is crucial for developing effective machine learning and deep learning models. While data augmentation is commonly used to increase the sample size, it does not guarantee data quality. Data generation goes beyond augmentation by incorporating additional steps to ensure high-quality output samples. This technique is particularly valuable for anomaly classification tasks with limited training samples. A recent study introduced a 3DVAE-LSTM (3-Dimensional Variational Autoencoders-Long Short-Term Memory) approach for generating extremely rare case signals. Although this framework synthesized samples for training deep learning models, it faced challenges with long sequential data. To address this, the authors proposed an improved version called i3DVAE-LSTM (Improvement of 3-Dimensional Variational Autoencoders-Long Short-Term Memory) and presented the evaluation of the i3DVAE-LSTM framework. The proposed framework adopts a divide-and-conquer technique, splitting long sequence data into smaller fragments to enhance the quality of generated samples, which are then concatenated together. Experimental results demonstrated that classification models trained with data generated by i3DVAE-LSTM outperformed baselines in all aspects.
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    3DVAE-LSTM for Extremely Rare Anomaly Signal Generation
    (2022-01-01)
    Kaewkiriya, Thongchai
    ;
    Woraratpanya, Kuntpong
    To overcome the uncontrolled quality output problem of data augmentation, many data generation frameworks have been proposed recently. The main concept of the data generation is for ensuring the quality of the output samples which maintain the original characteristics and highly provide the diversity of data. The benefit of this concept is improving the performance of deep learning tasks that suffer from the lack of available training samples, such as anomaly classification. Recently, 3D variational autoencoder for extremely rare case signal generation (3DVAE-ERSG) was introduced. This framework achieves the best synthesis samples for multi-class classification deep learning training. However, it is not so well applicable to sequential data. Therefore, this paper proposed a 3DVAE-LSTM framework. The new framework was replaced a VAE's feed-forward neural network with a long short-term memory (LSTM) neural network that works well with time-series signals. The experimental results show that the classification models trained with data generated by 3DVAE-LSTM have better performance than 3DVAE-ERSG in every aspect.
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    3DVAE-ERSG: 3D Variational Autoencoder for Extremely Rare Signal Generation
    (2021-01-01)
    Chalongvorachai, Thasorn
    ;
    Woraratpanya, Kuntpong
    Data generation is not data augmentation. Our data generation is a new technique that can synthesize a dataset from a very small number of samples and ensure the quality of its outputs. Recently, this concept has been proposed and applied in a framework called Data Generation Framework for Extremely Rare Case Signals (DGERS) to solve a problem of a limited number of anomaly signals. With the power of DGERS consisting of principal components, including various data augmentation techniques on diverse domains, Signal Fragment Assembler (SFA), Variational Autoencoder (VAE), Data Picker (DP), and Quality Classifier (QC), the generated dataset had the good quality, when evaluated with a performance tester. Nevertheless, the DGERS has not used the full potential of VAE yet. The previous framework used the VAE latent space in only two dimensions. To use a higher potential of VAE, this paper proposed a 3D Variational Autoencoder for Extremely Rare Signal Generation (3DVAE-ERSG). This method increases the dimension of the latent space from 2D to 3D. We also proposed the 3D Data Picker for data exploration. To test this hypothesis, we experimented with the same datasets that the DGERS method was tested before. The results show that our 3DVAE-ERSG can outperform the baseline in most cases.