Computer vision applications on the NVIDIA jetson platform
Video stabilization, a video enhancement technique which removes unwanted shake, is becoming increasingly important with the emergence of embedded systems with cameras. The NVIDIA Jetson platforms, claimed to be the cutting-edge solutions to embedded computer vision and machine learning, have bee...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/75087 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Video stabilization, a video enhancement technique which removes unwanted shake,
is becoming increasingly important with the emergence of embedded systems with
cameras. The NVIDIA Jetson platforms, claimed to be the cutting-edge solutions to
embedded computer vision and machine learning, have been commercially integrated
into moving platforms such as drones. This project investigated and proposed a complete
pipeline of video stabilization tasks, from motion estimation to video completion
in order to retain the resolution. Feature-based and block-matching methods are employed
in the estimation stage and Kalman filter is used to stabilize the motion. The
feature-based approach relies on Shi-Tomasi corner detector and Lucas-Kanade pyramidal
optical flow to estimate the motion. The block-matching method is extended
with brute-force search and interpolation to estimate the angle. To achieve real-time
processing, CUDA-accelerated codes are utilized for parallel computing. The result
is an application capable of processing at 41fps under resolution 640x360 and robust
against local motions. |
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