Traffic intensity monitoring using multiple object detection with traffic surveillance camera /
Roads in Malaysia are getting more congested every day and the problem does not seem to have a solution. Many researches are done to reduce the traffic congestion however there are no practical or real-time solution to the problem. Most researches are only able to simulate the traffic or rather gene...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Kuala Lumpur :
Kuliyyah of Engineering, International Islamic University Malaysia,
2018
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| Subjects: | |
| Online Access: | Click here to view 1st 24 pages of the thesis. Members can view fulltext at the specified PCs in the library. |
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| Summary: | Roads in Malaysia are getting more congested every day and the problem does not seem to have a solution. Many researches are done to reduce the traffic congestion however there are no practical or real-time solution to the problem. Most researches are only able to simulate the traffic or rather generalize the pattern into simpler arrival models, however this will not reflect on the actual road conditions. In this research, an alternative approach to measure the traffic intensity on the road is discussed. With computer vision has become a key business element in many corporations and the rise in the technology of cameras with lower costs of processing has enabled us to develop on most advanced system for many applications. Using traffic surveillance camera placed on roads and object detection algorithm, a new method of calculating the traffic intensity is developed and tested. Results of the test show the accuracy of about 80% for the algorithm to be able to tell the difference between number of cars and motorcycles. With this information, the road condition is estimated with higher accuracy. The result and performance are tabulated and some of the limitations are discussed in detail in the last chapter. However, there is still a lot of work need to be done until the application can run accurately in real-time. |
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| Physical Description: | xiii, 67 leaves : illustrations ; 30cm. |
| Bibliography: | Includes bibliographical references (leaves 63-66). |