Vehicle recognition from videos
With the high development of artificial intelligence, machine learning and pattern recognition are playing an increasingly important role in object detection and recognition from images or video sequences. This technique can help people to analyze and extract significant information of the image or...
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/72539 |
| _version_ | 1811686180001939456 |
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| author | Chen, Yang |
| author2 | Chau Lap Pui |
| author_facet | Chau Lap Pui Chen, Yang |
| author_sort | Chen, Yang |
| collection | NTU |
| description | With the high development of artificial intelligence, machine learning and pattern recognition are playing an increasingly important role in object detection and recognition from images or video sequences. This technique can help people to analyze and extract significant information of the image or video more efficiently and accurately. This dissertation conducts an experiment on vehicle detections from campus surveillance video sequences with Deep Learning Method using different datasets to pre-trained the network and compare the experimental results of each configuration. At first, we have searched and read materials about the development and state-of -art methods. After that with the help of Caffe framework and Python, we trained Neural Network with different types of datasets with different parameters and designed a Python program with Graphical User Interface (GUI) to show the results of detection. In the end, we compare the performance of detection results and find out that a fine-tuned pre-trained Neural Network can contribute to the improvement of detection performance. |
| first_indexed | 2024-10-01T04:56:19Z |
| format | Thesis |
| id | ntu-10356/72539 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:56:19Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/725392023-07-04T15:48:46Z Vehicle recognition from videos Chen, Yang Chau Lap Pui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering With the high development of artificial intelligence, machine learning and pattern recognition are playing an increasingly important role in object detection and recognition from images or video sequences. This technique can help people to analyze and extract significant information of the image or video more efficiently and accurately. This dissertation conducts an experiment on vehicle detections from campus surveillance video sequences with Deep Learning Method using different datasets to pre-trained the network and compare the experimental results of each configuration. At first, we have searched and read materials about the development and state-of -art methods. After that with the help of Caffe framework and Python, we trained Neural Network with different types of datasets with different parameters and designed a Python program with Graphical User Interface (GUI) to show the results of detection. In the end, we compare the performance of detection results and find out that a fine-tuned pre-trained Neural Network can contribute to the improvement of detection performance. Master of Science (Communications Engineering) 2017-08-28T10:14:09Z 2017-08-28T10:14:09Z 2017 Thesis http://hdl.handle.net/10356/72539 en 63 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Chen, Yang Vehicle recognition from videos |
| title | Vehicle recognition from videos |
| title_full | Vehicle recognition from videos |
| title_fullStr | Vehicle recognition from videos |
| title_full_unstemmed | Vehicle recognition from videos |
| title_short | Vehicle recognition from videos |
| title_sort | vehicle recognition from videos |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/72539 |
| work_keys_str_mv | AT chenyang vehiclerecognitionfromvideos |