Real-time object recognition through machine learning
Object recognition is a key fundamental function of computer vision and image processing. For many decades, a comprehensive study and exploration in the field of recognizing an object has been established. Up to the 21st century, it still is. The concept of ‘recognizing an object’ is used in many ap...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/68137 |
| _version_ | 1826120761521209344 |
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| author | Singh, Kelvin |
| author2 | Lim Meng Hiot |
| author_facet | Lim Meng Hiot Singh, Kelvin |
| author_sort | Singh, Kelvin |
| collection | NTU |
| description | Object recognition is a key fundamental function of computer vision and image processing. For many decades, a comprehensive study and exploration in the field of recognizing an object has been established. Up to the 21st century, it still is. The concept of ‘recognizing an object’ is used in many applications. The general course of action given some insights on the appearance of a specific object depends on the number of images (individual or more) studied thoroughly in line to assess the existing objects and their location. Nonetheless, each application has distinguishing requisites and restrictions. In this report, the focus is on the recognition of an Unmanned Aerial Vehicle (UAV). Nowadays, UAVs are becoming very popular. They are inexpensive, have the capabilities to hover with resistance of some degree, moves independently, being used for various applications and are efficient. One shortcoming is that when there is an abundance of UAVs, it would be difficult to recognize the UAVs in the sky among other things as UAVs varies in size from the size of an insect to that of a commercial airliner. This report documents how tracking and object recognition of an UAV is determined along with the development of progress to date of the Final Year Project (FYP). The various approaches, components, procedures and results are explained in detail. This is to meet all the possibilities and expected benchmark of the project outline. |
| first_indexed | 2024-10-01T05:21:43Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/68137 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:21:43Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/681372023-07-07T18:07:22Z Real-time object recognition through machine learning Singh, Kelvin Lim Meng Hiot School of Electrical and Electronic Engineering DRNTU::Engineering Object recognition is a key fundamental function of computer vision and image processing. For many decades, a comprehensive study and exploration in the field of recognizing an object has been established. Up to the 21st century, it still is. The concept of ‘recognizing an object’ is used in many applications. The general course of action given some insights on the appearance of a specific object depends on the number of images (individual or more) studied thoroughly in line to assess the existing objects and their location. Nonetheless, each application has distinguishing requisites and restrictions. In this report, the focus is on the recognition of an Unmanned Aerial Vehicle (UAV). Nowadays, UAVs are becoming very popular. They are inexpensive, have the capabilities to hover with resistance of some degree, moves independently, being used for various applications and are efficient. One shortcoming is that when there is an abundance of UAVs, it would be difficult to recognize the UAVs in the sky among other things as UAVs varies in size from the size of an insect to that of a commercial airliner. This report documents how tracking and object recognition of an UAV is determined along with the development of progress to date of the Final Year Project (FYP). The various approaches, components, procedures and results are explained in detail. This is to meet all the possibilities and expected benchmark of the project outline. Bachelor of Engineering 2016-05-24T06:50:03Z 2016-05-24T06:50:03Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/68137 en Nanyang Technological University 81 p. application/pdf |
| spellingShingle | DRNTU::Engineering Singh, Kelvin Real-time object recognition through machine learning |
| title | Real-time object recognition through machine learning |
| title_full | Real-time object recognition through machine learning |
| title_fullStr | Real-time object recognition through machine learning |
| title_full_unstemmed | Real-time object recognition through machine learning |
| title_short | Real-time object recognition through machine learning |
| title_sort | real time object recognition through machine learning |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/68137 |
| work_keys_str_mv | AT singhkelvin realtimeobjectrecognitionthroughmachinelearning |