Fish Tracking by Two-Staged Convolutional Neural Network Training and Length Measurement by PCA in Real Fishery Catch Event Stereo Video

Abstract

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

Date of Award	2017 Aug 3
Original language	English
Supervisor	Pau-Choo Chung (Supervisor)