Toward Deep Learning-Based Automated Speed and Line Change Detection System in Perspective of Bangladesh. Abdullah-Al-Mahmod, Usmani, S. A., Salam, M. A., Foyjul Haque Somrat, M., & Shamim Kaiser, M. In Kaiser, M. S., Bandyopadhyay, A., Ray, K., Singh, R., & Nagar, V., editors, Proceedings of Trends in Electronics and Health Informatics, of Lecture Notes in Networks and Systems, pages 351–361, Singapore, 2022. Springer. doi abstract bibtex 1 download Abdullah-Al-MahmodUsmani, Sabbir AhmedSalam, Mohammad AbdusFoyjul Haque Somrat, Md.Shamim Kaiser, M.In the smart city, major crossing and most part of the road will be under the CCTV surveillance system. This influenced the community to investigate a vision-based speed and line change detection system for traffic management in the city, ensuring both road safety and efficient road design. In this paper, we proposed a deep learning model for detecting vehicle type, speed and abrupt line change using the CCTV footage in real-time. The faster region-based convolutional neural network (fr-CNN) model is chosen in this scenario, which demonstrates amazing performance in object detection. The model is trained and validated using data acquired from a self-created traffic dataset from Dhaka. According to the results of the performance evaluation, the suggested fr-CNN model for moving vehicle status detection system outperforms the mobile-net single-shot multibox detection technique in terms of overall performance.
@inproceedings{abdullah-al-mahmod_toward_2022,
address = {Singapore},
series = {Lecture {Notes} in {Networks} and {Systems}},
title = {Toward {Deep} {Learning}-{Based} {Automated} {Speed} and {Line} {Change} {Detection} {System} in {Perspective} of {Bangladesh}},
isbn = {9789811688263},
doi = {10.1007/978-981-16-8826-3_30},
abstract = {Abdullah-Al-MahmodUsmani, Sabbir AhmedSalam, Mohammad AbdusFoyjul Haque Somrat, Md.Shamim Kaiser, M.In the smart city, major crossing and most part of the road will be under the CCTV surveillance system. This influenced the community to investigate a vision-based speed and line change detection system for traffic management in the city, ensuring both road safety and efficient road design. In this paper, we proposed a deep learning model for detecting vehicle type, speed and abrupt line change using the CCTV footage in real-time. The faster region-based convolutional neural network (fr-CNN) model is chosen in this scenario, which demonstrates amazing performance in object detection. The model is trained and validated using data acquired from a self-created traffic dataset from Dhaka. According to the results of the performance evaluation, the suggested fr-CNN model for moving vehicle status detection system outperforms the mobile-net single-shot multibox detection technique in terms of overall performance.},
language = {en},
booktitle = {Proceedings of {Trends} in {Electronics} and {Health} {Informatics}},
publisher = {Springer},
author = {{Abdullah-Al-Mahmod} and Usmani, Sabbir Ahmed and Salam, Mohammad Abdus and Foyjul Haque Somrat, Md. and Shamim Kaiser, M.},
editor = {Kaiser, M. Shamim and Bandyopadhyay, Anirban and Ray, Kanad and Singh, Raghvendra and Nagar, Vishal},
year = {2022},
keywords = {CNN, Object detection, Open CV, Road-traffic, Single-shot multibox detection, Tensor flow},
pages = {351--361},
}
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