Sample Imbalance Adjustment and Similar Object Exclusion in Underwater Object Tracking

Although modern trackers exhibit competitive performance for underwater image degradation assessment, two problems remain when these are applied to underwater object tracking (UOT). A single-object tracker is trained on open-air datasets, which results in a serious sample imbalance between underwater objects and open-air objects when it is applied to UOT. Moreover, underwater targets such as fish and dolphins usually have a similar appearance, and it is challenging for models to discriminate weak discriminative features. Existing detection-based post-processing approaches struggle to distinguish a tracked target from similar objects. In this study, the UOSTrack is proposed, which involves the use of underwater images and open-air sequence hybrid training (UOHT), and motion-based post-processing (MBPP). The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker. In particular, underwater object detection (UOD) images are converted into image pairs through customised data augmentation, such that the tracker is exposed to more underwater domain training samples and learns the feature expressions of underwater objects. The MBPP paradigm is proposed to exclude similar objects near the target. In particular, it employs the estimation box predicted using a Kalman filter and the candidate boxes in each frame to reconfirm the tracked target that is hidden in the candidate area when it has been lost. UOSTrack provides an average performance improvement of 3.5 % compared to OSTrack on similar object challenge attribute in UOT100 and UTB180. The average performance improvements provided by UOSTrack are 1 % and 3 %, respectively. The results from two UOT benchmarks demonstrate that UOSTrack sets a new state-of-the-art benchmark, and the effectiveness of UOHT and MBPP, and the generalisation and applicability of the MBPP for use in UOT

Novel deep learning architectures for marine and aquaculture applications

Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices

Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehicles

In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.Comment: To appear in IJCV SI: Animal Trackin