Real-Time Littering Activity Monitoring Based on Image Classification Method

Husni, Nyayu Latifah; Sari, Putri Adelia Rahmah; Handayani, Ade Silvia; Dewi, Tresna; Seno, Seyed Amin Hosseini; Caesarendra, Wahyu; Głowacz, Adam; Oprzędkiewicz, Krzysztof; Sułowicz, Maciej

doi:10.3390/smartcities4040079

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…The second novelty is ambient and motion descriptor extraction through a graph-based approach that helps extract robust descriptors related to the data type. The comparative results for the proposed IoHT-based HLD system with the first novelty, second novelty, and both together are given in Table 1, along with a comparison of the same system classification through the CNN [90] and LSTM [91].…”

Section: Experiments 1: Evaluation Protocolmentioning

confidence: 99%

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Javeed,

Abdelhaq,

Algarni

et al. 2023

Micromachines

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Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.

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Section: Experiments 1: Evaluation Protocolmentioning

confidence: 99%

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Javeed,

Abdelhaq,

Algarni

et al. 2023

Micromachines

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“…This activity was recorded for 2 to 6 min, with two types of activity, i.e., littering or not littering (just passing through the area or, in this study, normal). The dataset obtained in this research was combined with the dataset from previous research [9]. All videos were extracted into multiple images and labeled as "littering" and "normal".…”

Section: Datasetmentioning

confidence: 99%

“…In the proposed method, an RNN and pose detection were used to recognize the positions of elbows, hips, hands, knees, and feet to form a body pose framework by combining these points and predicting an estimate of the detected pose. The advantage of using an RNN is that it can process data sequences, such as several video frames [9]. The main idea of RNNs is that they can share parameters across multiple parts of the model.…”

Section: Introductionmentioning

confidence: 99%

Pose Detection and Recurrent Neural Networks for Monitoring Littering Violations

Husni,

Felia,

Abdurrahman

et al. 2023

Eng

Self Cite

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Infrastructure development requires various considerations to maintain its continuity. Some public facilities cannot survive due to human indifference and irresponsible actions. Unfortunately, the government has to spend a lot of money, effort, and time to repair the damage. One of the destructive behaviors that can have an impact on infrastructure and environmental problems is littering. Therefore, this paper proposes a device as an alternative for catching littering rule violators. The proposed device can be used to monitor littering and provide warnings to help officers responsible for capturing the violators. In this innovation, the data obtained by the camera are sent to a mini-PC. The device will send warning information to a mobile phone when someone litters. Then, a speaker will turn on and issue a sound warning: “Do not litter”. The device uses pose detection and a recurrent neural network (RNN) to recognize a person’s activity. All activities can be monitored in a more distant place using IoT technology. In addition, this tool can also monitor environmental conditions and replace city guards to monitor the area. Thus, the municipality can save money and time.

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“…Dalam penelitian tersebut, memberikan gambaran mengenai alat pengukur suhu tanpa sentuh dengan menggunakan komponen identik dengan cara kerja serupa yaitu melakukan pengukuran suhu berdasarkan penggunaan pancaran sinar inframerah serta menggunakan basis mikrokontroler yang termasuk ke dalam keluarga Arduino. Webcam [5] berfungsi sebagai input yang menghasilkan image, raspberry pi berfungsi sebagai pengelolah image dari webcam [6][7]. Hasil image tersebut diolah dengan bahasa pemrograman python untuk mendapatkan posisi deteksi yang meliputi wajah, mata dan mulut.…”

Section: Pendahuluanunclassified

Pendeteksi Masker dan Monitoring Suhu menggunakan Webcam dan Sensor Suhu GY-906 untuk Pencegahan Penularan Covid-19

Evelina

Putra

Husni

et al. 2022

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is a pandemic that is happening all over the world. The use of masks, keeping a distance and monitoring body temperature are ways that can be done to prevent the transmission of the Covid-19 virus. However, unfortunately, people often don't obey this rule because they underestimate the Covid-19 problem. Based on this, the author tries to develop a system that is able to detect and monitor the use of masks and human body temperature automatically. The design of this detection system uses a webcam connected to a raspberry pi, a GY-906 Temperature Sensor, and Proximity Sensor HC-SR04 that is connected with arduino nano. The detection system will detect whether the user is wearing a mask or not, and detect whether the user's body temperature is normal or not. The output of this detection system is a display on the monitor and LCD as well as other outputs, such as LEDs and Buzzers. From the test results of the device, it is found that the device can work well

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Real-Time Littering Activity Monitoring Based on Image Classification Method

Cited by 8 publications

References 31 publications

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Pose Detection and Recurrent Neural Networks for Monitoring Littering Violations

Pendeteksi Masker dan Monitoring Suhu menggunakan Webcam dan Sensor Suhu GY-906 untuk Pencegahan Penularan Covid-19

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