Smart surveillance system for identifying bikers without helmets using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| Acceso en liña: | http://hdl.handle.net/10361/13850 |
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10361-138502022-01-26T10:20:01Z Smart surveillance system for identifying bikers without helmets using deep learning Hossain, MD. Iqbal Muhib, Raghib Barkat Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Surveillance system Deep learning Convolutional Neural Networks(CNNs) Helmet License plate Tensorflow Tesseract Object detection Video surveillance. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 49-50). Modern world is progressing quickly along with technology and one of the major sectors is transportation technology. Day by day the number of people are increasing and the number of vehicles are increasing too. As a result we have easy access to vehicles these days but at the same time it has increased the number of road accidents. Among the various types of road accidents, motorcycle accident is one of the main type which causes severe injuries and in some cases death. The only protection motorcyclists use is their helmet. Most countries has a law on wearing helmet otherwise it will be punished, but many people often break this law and as a result it increases the percentage of severe injury and death. In a populated country it's hard to keep track of the bikers who don't use helmets because of the huge number of bikes moving at a time. In this circumstances, we have developed a solution which can identify the bikers who don't use helmets. Our system uses image processing and deep Convolutional Neural Networks(CNNs) which is used to identify who breaks the law of wearing helmet, with helmet vs without helmet identification and finally motorcycle license plate recognition. We have used multiple models of object identification and evaluated in terms of speed vs accuracy. The results we have got indicates that due to the increase of law enforcement and awareness of the traffic police, the use of helmet has decreased a bit than before but there are still a lot of bikers who don't use helmets on regular basis. As we are going to detect people without helmet and then will keep the registration number from the license plate, we can easily identify the law breakers and the government can punish them accordingly. We have used the Tensor ow library for our system. The models we have used in our system are SSD Mobilenet v2 and Faster RCNN inception v2. For SSD Mobilenet V2 the accuracy for the helmet was 90 percent, human was 55 percent, bike was 80 percent and number plate was 95 percent. This model is quite light and we can use this trained model even in mobile devices. For Faster RCNN inception v2 it took more computations but the accuracy we got was slightly better as it is more heavyweight than SSD Mobilenet. The accuracy we got for this was 92 percent for the helmet, 58 percent for the human, 81 percent for bikes and 96 percent for number plates. MD. Iqbal Hossain Raghib Barkat Muhib B. Computer Science 2020-03-15T10:30:23Z 2020-03-15T10:30:23Z 2019 2019-08 Thesis ID 15301060 ID 15301058 http://hdl.handle.net/10361/13850 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 50 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Surveillance system Deep learning Convolutional Neural Networks(CNNs) Helmet License plate Tensorflow Tesseract Object detection Video surveillance. |
| spellingShingle |
Surveillance system Deep learning Convolutional Neural Networks(CNNs) Helmet License plate Tensorflow Tesseract Object detection Video surveillance. Hossain, MD. Iqbal Muhib, Raghib Barkat Smart surveillance system for identifying bikers without helmets using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Hossain, MD. Iqbal Muhib, Raghib Barkat |
| format |
Thesis |
| author |
Hossain, MD. Iqbal Muhib, Raghib Barkat |
| author_sort |
Hossain, MD. Iqbal |
| title |
Smart surveillance system for identifying bikers without helmets using deep learning |
| title_short |
Smart surveillance system for identifying bikers without helmets using deep learning |
| title_full |
Smart surveillance system for identifying bikers without helmets using deep learning |
| title_fullStr |
Smart surveillance system for identifying bikers without helmets using deep learning |
| title_full_unstemmed |
Smart surveillance system for identifying bikers without helmets using deep learning |
| title_sort |
smart surveillance system for identifying bikers without helmets using deep learning |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/13850 |
| work_keys_str_mv |
AT hossainmdiqbal smartsurveillancesystemforidentifyingbikerswithouthelmetsusingdeeplearning AT muhibraghibbarkat smartsurveillancesystemforidentifyingbikerswithouthelmetsusingdeeplearning |
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