Multiple animal tracking based on deep learning is a crucial and demanding task with
extensive applications in agriculture, livestock farming, and ecological research. The primary …

Abstract We propose a Cascaded Buffered IoU (C-BIoU) tracker to track multiple objects that
have irregular motions and indistinguishable appearances. When appearance features are …

Multi-object tracking (MOT) aims to associate target objects across video frames in order to
obtain entire moving trajectories. With the advancement of deep neural networks and the …

Abstract We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for
detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich …

Current multi-object tracking (MOT) aims to predict trajectories of targets (ie,“where”) in
videos. Yet, knowing merely “where” is insufficient in many crucial applications. In …

The complex dynamicity of open-world objects presents non-negligible challenges for multi-
object tracking (MOT) often manifested as severe deformations fast motion and occlusions …

Abstract Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for
animal motion and behavior analysis and has many crucial applications such as biology …

In the context of smart aquaculture, real-time Multi-fish Tracking (MFT) poses a significant
challenge. Existing Multi-Object Tracking (MOT) methods are often designed for objects with …

This paper presents a new large scale multi-person tracking dataset. Our dataset is over an
order of magnitude larger than currently available high quality multi-object tracking datasets …

3D Multi-Object Tracking (MOT) in dynamic point cloud sequences is a fundamental
research problem for several downstream tasks such as motion planning and action …