Data-driven Online Group Detection Based on Structured Prediction
Yingli Zhao, Zhengxi Hu, Lei Zhou, Meng Liu, Jingtai Liu · 2020
Group detection of crowds is an important and challenging problem in applications of the crowds analysis. Especially for service robots, accurate group detection is the premise to ensure the safe interaction between humans and robots. In this paper, we propose an online group detection method based on Structured Prediction for middle density crowds. First of all, we extend the features of pairwise trajectories with velocity and orientation to obtain more valid information. Then, a fully-connected social network is maintained to improve time efficiency significantly. Finally, we adopt the adaptive-sampling BCFW algorithm to learn the mapping from trajectories to groups. Comparing with current state-of-the-art methods, our experiments demonstrate the group detection capacity on precision and time efficiency.