Depthwise Separable Convolutional Neural Networks for Pedestrian Attribute Recognition

Junejo, Imran N.; Ahmed, Naveed

doi:10.1007/s42979-021-00493-z

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…The overall number of learnable parameters in our system is 6,844, as opposed to 14,362 for the very same system using traditional convolutions. We chose this particular DS-CNN because of its demonstrated versatility, training efficacy, low parameter bank, and impressive performance on smaller samples ( 25 ).…”

Section: Methodsmentioning

confidence: 99%

Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

Asghar

Albogamy

Al-Rakhami

et al. 2022

Front. Public Health

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Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).

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Section: Methodsmentioning

confidence: 99%

Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

Asghar

Albogamy

Al-Rakhami

et al. 2022

Front. Public Health

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“…However, the handcrafted feature was used in studies [35] and [34], which could not represent images effectively on surveillance cameras. In research [36], the Depthwise Separable Convolution method achieved a recall value of 72.07 and an F1 score of 66.60. In the study [37], multi-visual feature recognition with multi-label focal loss was carried out, producing 84.83% mA (mean accuracy), 79.37% accuracy, 87.47% precision, 86.09% recall, and 86.77% F1-score.…”

Section: Body Visual Multi-feature Recognitionmentioning

confidence: 96%

Real-Time Human Tracking Using Multi-Features Visual With CNN-LSTM and Q-Learning

Maharani,

Machbub,

Rusmin

et al. 2024

IEEE Access

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Various methods are employed in computer vision applications to identify individuals, including using face recognition as a human visual feature helpful in tracking or searching for a person. However, tracking systems that rely solely on facial information encounter limitations, particularly when faced with occlusions, blurred images, or faces oriented away from the camera. Under these conditions, the system struggles to achieve accurate tracking-based face recognition. Therefore, this research addresses this issue by fusing descriptions of the face visual with body visual features. When the system cannot find the target face, the CNN+LSTM hybrid method assists in multi-feature body visual recognition, narrowing the search space and speeding up the search process. The results indicate that the combination of the CNN+LSTM method yields higher accuracy, recall, precision, and F1 scores (reaching 89.20%, 87.36%, 91.02%, and 88.43%, respectively) compared to the single CNN method (reaching 88.84%, 74.00%, 67.00%, and 69.00% respectively). However, the combination of these two visual features requires high computation. Thus, it is necessary to add a tracking system to reduce the computational load and predict the location. Furthermore, this research utilizes the Q-Learning algorithm to make optimal decisions in automatically tracking objects in dynamic environments. The system considers factors such as face and body visual features, object location, and environmental conditions to make the best decisions, aiming to enhance tracking efficiency and accuracy. Based on the conducted experiments, it is concluded that the system can adjust its actions in response to environmental changes with better outcomes. It achieves an accuracy rate of 91.5% and an average of 50 fps in five different videos, as well as a video benchmark dataset with an accuracy of 84% and an average error of 11.15 pixels. Utilizing the proposed method speeds up the search process and optimizes tracking decisions, saving time and computational resources.

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Pedestrian Attribute Recognition with Feature Combination in Transformer with Attention Model

Tang

Lin²

2022

Lecture Notes in Electrical Engineering

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Depthwise Separable Convolutional Neural Networks for Pedestrian Attribute Recognition

Cited by 9 publications

References 35 publications

Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

Real-Time Human Tracking Using Multi-Features Visual With CNN-LSTM and Q-Learning

Pedestrian Attribute Recognition with Feature Combination in Transformer with Attention Model

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