19 citations · 19 across the 4 of their papers we have counts for
4 papers
IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction
Yuehai Chen
Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians…
Learning Discriminative Features for Crowd Counting
Yuehai Chen, Qingzhong Wang, Jing Yang +3
Crowd counting models in highly congested areas confront two main challenges: weak localization ability and difficulty in differentiating between foreground and background, leading…
Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map
Yuehai Chen, Jing Yang, Badong Chen +2
Counting objects in crowded scenes remains a challenge to computer vision. The current deep learning based approach often formulate it as a Gaussian density regression problem. Suc…
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting
Yuehai Chen, Jing Yang, Dong Zhang +3
Background noise and scale variation are common problems that have been long recognized in crowd counting. Humans glance at a crowd image and instantly know the approximate number…