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Found 16 papers, 6 papers with code
Exploring Learning-based Motion Models in Multi-Object Tracking
In the field of multi-object tracking (MOT), traditional methods often rely on the Kalman Filter for motion prediction, leveraging its strengths in linear motion scenarios.
A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video
Dense object counting or crowd counting has come a long way thanks to the recent development in the vision community.
Tree Counting by Bridging 3D Point Clouds with Imagery
We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees.
UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning
In this paper, we propose UniHPE, a unified Human Pose Estimation pipeline, which aligns features from all three modalities, i. e., 2D human pose estimation, lifting-based and image-based 3D human pose estimation, in the same pipeline.
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV).
Ranked #1 on
Semantic Segmentation
on LaRS
Efficient Domain Adaptation via Generative Prior for 3D Infant Pose Estimation
We further apply a guided diffusion model to domain adapt 3D adult pose to infant pose to supplement small datasets.
Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking
Re-identification (ReID) in multi-object tracking (MOT) for UAVs in maritime computer vision has been challenging for several reasons.
Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation
Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with significantly better performance in most benchmarks than traditional optimization-based methods.
Ranked #10 on
3D Human Pose Estimation
on 3DPW
(PA-MPJPE metric)
MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling
In this paper, we propose MPM, a unified 2D-3D human pose representation framework via masked pose modeling.
Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment
Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs.
Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring
The aim of in-trawl catch monitoring for use in fishing operations is to detect, track and classify fish targets in real-time from video footage.
Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation
We observe that the degradation is caused by two factors: 1) the large distribution gap over global positions of poses between the source and target datasets due to variant camera parameters and settings, and 2) the deficient diversity of local structures of poses in training.
3D Human Pose Estimation
3D Human Pose Estimation in Limited Data
+3
CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations
By adding a camera parameter branch, any in-the-wild 2D annotations can be fed into our pipeline to boost the training diversity and the 3D poses can be implicitly learned by reprojecting back to 2D.
Ranked #68 on
3D Human Pose Estimation
on Human3.6M
Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data
Due to limited public infant-related datasets, many works use the SMIL-based method to generate synthetic infant images for training.
RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization
Finally, we propose a method to evaluate the object detection performance of the RODNet.
RODNet: Radar Object Detection Using Cross-Modal Supervision
Radar is usually more robust than the camera in severe driving scenarios, e. g., weak/strong lighting and bad weather.
