most citedLocalization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections

5 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20235 cited

Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections

Ting Meng, Chunyun Fu, Mingguang Huang +4

In currently available literature, no tracking-by-detection (TBD) paradigm-based tracking method has considered the localization confidence of detection boxes. In most TBD-based me…

cs.CV2023

Tracking Objects with 3D Representation from Videos

Jiawei He, Lue Fan, Yuqi Wang +4

Data association is a knotty problem for 2D Multiple Object Tracking due to the object occlusion. However, in 3D space, data association is not so hard. Only with a 3D Kalman Filte…

cs.CV20231 cited

3D Video Object Detection with Learnable Object-Centric Global Optimization

Jiawei He, Yuntao Chen, Naiyan Wang +1

We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels ac…

cs.CV20233 cited

3D Multi-Object Tracking Based on Uncertainty-Guided Data Association

Jiawei He, Chunyun Fu, Xiyang Wang

In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calcul…

cs.CV20221 cited

Densely Constrained Depth Estimator for Monocular 3D Object Detection

Yingyan Li, Yuntao Chen, Jiawei He +1

Estimating accurate 3D locations of objects from monocular images is a challenging problem because of lacking depth. Previous work shows that utilizing the object's keypoint projec…