Learning Object Bounding Boxes for 3D Instance Segmentation on Point
B. Yang, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, N.
Neural Information Processing Systems (NeurIPS), 2019 (Spotlight, 200/6743)
We propose a simple and efficient neural
architecture for accurate 3D instance segmentation on point clouds.
It achieves the SOTA performance on ScanNet and S3DIS (June 2019).
Robust Long-term Tracking via Instance Specific Proposals
H. Liu, Q. Hu, B. Li, Y. Guo
IEEE Transactions on Instrumentation and Measurement (IEEE TIM), 2019 (IF=3.06)
We propose an efficient and robust tracker for
long-term object tracking, which is based on instance specific proposals. In particular,
an instance-specific proposal generator is embedded into the error correction module to
recover lost target from tracking failures.
Semi-Online Multiple Object Tracking Using Graphical Tracklet Association
J. Wang, Y. Guo, X. Tang, Q. Hu, W. An
IEEE Signal Processing Letters (IEEE SPL), 2018 (IF=3.27)
We propose a semi-online MOT method using online
discriminative appearance learning and tracklet association with a sliding window. We
connect similar detections of neighboring frames in a temporal window, and improve the
performance of appearance feature by online discriminative appearance learning. Then,
tracklet association is performed by minimizing a subgraph decomposition cost.
Object tracking using multiple features and adaptive model updating
Q. Hu, Y. Guo, Z. Lin, W. An, H. Cheng
IEEE Transactions on Instrumentation and Measurement (IEEE TIM), 2018 (IF=3.06)
We proposea to combine a 2-D location filter with
a 1-D scale filter to jointly estimate the state of object under tracking, and three
complementary features are integrated to further enhance the overall tracking
performance. A penalty factor is also defined to achieve a balance between
stability and flexibility, especially when the object is under occlusion.
Long-term Object Tracking with Instance Specific Proposals
H. Liu, Q. Hu, B. Li, Y. Guo
24th International Conference on Pattern Recognition (ICPR), 2018
We propose a tracker named Complementary
Learners with Instance-specific Proposals (CLIP). The CLIP tracker consists of a
translation filter, a scale filter, and an error correction module. The error correction
module is activated to correct the localization error by an instance-specific proposal
generator, especially when the target suffers from dramatic appearance changes.
Correlation Filter Tracking: Beyond an Open-loop System
Q. Hu, Y. Guo, Y. Chen, J. Xiao, W. An
British Machine Vision Conference (BMVC), 2017
We interpret object tracking as a closed-loop
tracking problem, and add a feedback loop to the tracking process by introducing an
efficient method to estimate the localization error. We propose a generic
self-correction mechanism for CF based trackers by introducing a closed-loop feedback
Robust and real-time object tracking using scale-adaptive correlation
Q. Hu, Y. Guo, Z. Lin, X. Deng, W. An
Digital Image Computing: Techniques and Applications
(DICTA), 2016 (Oral)
We represent the target in kernel feature space
and train a classifier on a scale pyramid to achieve adaptive scale estimation. We then
integrate three complementary features to further enhance the overall tracking