3 accepted papers
Predicting the future actions of multiple pedestrians is an essential feature for autonomous robots co-working in human crowded-environments. Estimating the unknown future path is a challenging problem due to the complex interactions occurring among pedestrians. Although recent developments in Graph
Forecasting future trajectories of multiple pedestrians in a crowded environment is a challenging problem due to the complex interactions among the pedestrians. The interactions can be asymmetric and their influences may vary over time. Moreover, each pedestrian can exhibit different behavior at any…
Multi-human tracking in the crowded environment is a challenging problem due to occlusions, pose change, viewpoint variation and cluttered background. In this work, we propose a robust feature learning for tracking-by-detection methods based on second-order attention network that can capture higher-…