碩士 / 國立成功大學 / 電腦與通信工程研究所 / 105 / The purpose of the thesis is use the state-of-the-art real time object detector to detect the rail catch event and combine with RGB-D camera to track the fish and estimate the length in 3D space. EM (Electronic monitoring) for fishery activities has drawn increasing attention. In the wild sea surface, contains dynamic background, noise from the sea water and deformable objects, however, make conventional tracking and segmentation methods unreliable. In this thesis, we take advantage on deep learning and convolutional neural network in this work. we present a tracking and segmentation system in stereo video for monitoring fish catching on wild sea surface. Based on the result of a state-of-the-art pre-trained real time convolutional neural network object detector. Since the CNN(Convolutional Neural Network) object detector is based on frame-by-frame to detect the object. It will be not a continuous tracking for each object. In other words, it will cause a not-continuous missing frame in some cases due to the detection confidence is not higher than threshold. To deal with that problem and to make the system more reliable, the Kalman filtering-based tracking system is used to rescore the multiple object proposals and track the objects. Which will fill those missing frames cause by the CNN, and also makes the length measurement result more robust. Then, to segment the objects, we first apply a sampling-and-scoring strategy to classify the background plane based on background subtraction and disparity map, and then refine the segmentation of objects using color and geometric features. With the segmentation results, we can measure the 3D lengths of objects and help the tracking system as well. Experimental results show that a reliable tracking and measurement performance under noisy and dynamic environment is achieved.
| Identifer | oai:union.ndltd.org:TW/105NCKU5652039 |
| Date | January 2017 |
| Creators | Sheng-TingShen, 沈昇廷 |
| Contributors | Pau-Choo Chung, 詹寶珠 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 52 |
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