A Lightweight Moving Vehicle Classification System Through Attention-Based Method and Deep Learning

Nasaruddin, Nasaruddin; Muchtar, Kahlil; Afdhal, Afdhal

doi:10.1109/access.2019.2950162

Cited by 14 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Experimental results show that their method is superior to other advanced methods. Nasaruddin et al [6] introduced a new attention-based approach in order to clearly distinguish between areas of interest (moving cars) and areas of disinterest (the rest of the region), and input the respective areas of interest into deep CNN. They used several challenging outdoor sequences from CDNET 2014 (baseline, severe weather, and camera shake classes), as well as their own data set, to evaluate the proposed approach.…”

Section: Related Workmentioning

confidence: 99%

A vehicle classification method based on deep learning and multi-attention mechanism

Zhang¹,

Feng²

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

Deep learning has many important applications in autonomous vehicle classification. However, due to the similarity between different types of vehicles, the deep learning model is faced with a certain degree of challenge in classifying vehicles. If the deep learning method can be improved to improve the accuracy of vehicle classification method, it will be very helpful for the practical application of autonomous driving. In this paper, ResNet34 deep learning model is selected as the backbone network, and FcaNet and several spatial attention mechanisms are added to improve the model. We tested our proposed approach on a data set containing 10 different types of vehicles. The test results on the vehicle classification data set show that the classification accuracy of the improved scheme reaches 79.0%, which is higher than the 75.42% of the original ResNet34 model. The experimental results show that the method of adding multiple attention mechanisms to ResNet deep learning network is helpful to improve the classification accuracy of different vehicles.

show abstract

Section: Related Workmentioning

confidence: 99%

A vehicle classification method based on deep learning and multi-attention mechanism

Zhang¹,

Feng²

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

show abstract

“…: Substraksi Latar Menggunakan Nilai Mean untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning 130 performa (performance evaluation). Hasil perbandingan piksel-piksel berupa nilai dapat diklasifikasikan dalam sebuah matriks yang disebut confusion matrix [14] seperti ditunjukkan pada Tabel 1.…”

Section: Prosedur Pengujianunclassified

Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning

Mahdi

Muchtar

Arnia

et al. 2022

JRE

Self Cite

View full text Add to dashboard Cite

Teknik pengolahan citra digital saat ini sudah sangat banyak penerapannya dalam berbagai aplikasi. Teknik ini tidak hanya terbatas digunakan pada proses pengenalan foto atau gambar saja, melainkan juga sudah mulai diterapkan dalam pengenalan gambar bergerak atau biasa disebut video. Di antara pemanfaatan teknik pengolahan citra pada video adalah adanya penggunaan sistem deteksi gerak dan sistem pengawasan objek diam maupun objek yang bergerak. Sistem pengawasan objek di dalam video seringkali diterapkan untuk mengawasi objek tertentu pada suatu tempat dengan menggantikan peran manusia dalam pengawasan [1].Dalam sistem deteksi objek bergerak, objek tersebut dapat dideteksi dengan cara memanfaatkan informasi dari kumpulan frame pada sebuah video. Metode yang paling umum digunakan dalam pendekatan spatial-temporal information adalah metode frame difference. Cara kerja metode ini adalah dengan membandingkan kemudian mengurangi latar dari frame yang sekarang dengan frame yang sudah ada sebelumnya dari beberapa urutan frame dari sebuah video. Apabila selisih antara piksel sekarang dengan piksel dari frame sebelumnya bernilai lebih besar

show abstract

“…The developed softmax regression classifier includes higher-level layers; however, by embedding lower-resolution vehicle images, there may be a loss of vehicle type information. Nasaruddin et al [23] developed an attention-based approach and a deep CNN technique for lightweight moving vehicle classification. In this literature, the developed model performance was validated on a real-time dataset by means of specificity, precision, and f -score.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Jagannathan¹,

Sujatha

Frnda

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

show abstract

A Lightweight Moving Vehicle Classification System Through Attention-Based Method and Deep Learning

Cited by 14 publications

References 24 publications

A vehicle classification method based on deep learning and multi-attention mechanism

A vehicle classification method based on deep learning and multi-attention mechanism

Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Contact Info

Product

Resources

About