This review explores the application of Long Short-Term Memory (LSTM) networks, a
specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM …

Abstract A novel Maritime Multi-Object Tracking method is proposed, combining a deep
learning-based object detector with target association algorithms to achieve robust sea …

Cell tracking remains a pivotal yet challenging task in biomedical research. The full potential
of deep learning for this purpose is often untapped due to the limited availability of …

Wildlife conservationists have historically depended on manual methods for the
identification and tracking of avian species, to monitor population dynamics and discern …

The paper presents a novel approach to enhancing multi-object detection and tracking in
video sequences using a Modified Ant Swarm Optimization Deep Learning (ASO-DL) …

Multi-object tracking (MOT) is a critical technology in computer vision, designed to detect
multiple targets in video sequences and assign each target a unique ID per frame. Existed …

Multi-object tracking (MOT) has seen rapid improvements in recent years. However, frequent
occlusion remains a significant challenge in MOT, as it can cause targets to become smaller …

The integration of artificial intelligence (AI) and blockchain technology offers promising
advancements in face detection and human tracking within Internet of Things (IoT) …

This work addresses the critical challenge of detecting objects in video captured by UAVs to
assist in maritime monitoring/surveillance. The objective is to take advantage of the spatial …

Multi-object tracking (MOT) technology plays a key role in intelligent sports event analysis,
and is essential for automating athlete data collection, assisting referee decision-making …