Mouse pose estimations have important applications in the fields of animal behavior research, biomedicine, and conservation studies. Accurate efficient mouse using computer vision are necessary. Although methods for developed, bottlenecks still exist. One most prominent problems is lack uniform standardized training datasets. Here, we resolve this difficulty by introducing dataset. Our dataset ...

in this paper, we propose a novel method for fully automatic detection and tracking of human heads and faces in video sequences. the proposed algorithm consists of two modules: a face detection module and a face tracking module. the detection module, detects the face region and approximates it with an ellipse at the first frame using a modified version of adaboost cascaded classifier. the detec...

In this paper, a feature-based technique for the camera pose estimation in a sequence of wide-baseline images has been proposed. Camera pose estimation is an important issue in many computer vision and robotics applications, such as, augmented reality and visual SLAM. The proposed method can track captured images taken by hand-held camera in room-sized workspaces with maximum scene depth of 3-4...

Pose estimation involves estimating the position and orientation of objects in a 3D space, it has applications areas such as robotics, augmented reality, human-computer interaction. There are several methods for pose estimation, including model-based, feature-based, direct, hybrid, deep learning-based methods. Each method its own strengths weaknesses, choice depends on specific requirements app...

Human pose estimation has achieved significant progress on images with high imaging resolution. However, low-resolution imagery data bring nontrivial challenges which are still under-studied. To fill this gap, we start investigating existing methods and reveal that the most dominant heatmap-based would suffer more severe model performance degradation from low-resolution, offset learning is an e...

The ultimate goal for an inference model is to be robust and functional in real life applications. However, training vs. test data domain gaps often negatively affect performance. This issue especially critical the monocular 3D human pose estimation problem, which collected a controlled lab setting. In this paper, we focus on alleviating negative effect of shift both appearance space by present...