Tracking of Underwater Objects with Occlusion Awareness using an Adaptive DEEP SORT and GMM Approach

Article Computer Science, Information Systems

Deep Neural Network Driven Automated Underwater Object Detection

Ajisha Mathias et al.

Summary: Object recognition and computer vision techniques are used for automated fish abundance estimation in marine environments. The combination of visual features, Gaussian mixture models, and YOLOv3 deep network improves the efficiency of underwater object detection.

CMC-COMPUTERS MATERIALS & CONTINUA (2022)

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Article Ecology

Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian Mixture Model approach

Ajisha Mathias et al.

Summary: A new approach of underwater object detection is proposed by using the BEMD algorithm to extract features and generate blob, then adapting to the GMM for object monitoring system. The proposed method is primarily developed and tested for fish detection, with qualitative and quantitative comparisons analysis against state-of-art object detection schemes.

ECOLOGICAL INFORMATICS (2021)

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Article Fisheries

Tracking industrial fishing activities in African waters from space

Mi-Ling Li et al.

Summary: Marine fisheries in African waters play a significant role in food security and local economies, but face challenges such as climate change, pollution, and overexploitation. By utilizing AIS-derived data, we can effectively analyze the spatial characteristics of industrial fishing activities in African waters and the impact of different fishing strategies on fishing effort.

FISH AND FISHERIES (2021)

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Article Engineering, Ocean