People detection in crowded scenes by context-driven label propagation

Exploiting contextual cues has been a key idea to improve people detection in crowded scenes. Along this line we present a novel context-driven approach to detect people in crowded scenes. Based on a context graph that incorporates both geometric and social contextual patterns in crowds, we apply label propagation to discover weak detections contextually compatible with true detections while suppressing irrelevant false alarms. Compared to previous approaches for context modeling limited to only pairwise spatial interactions between local object neighbors, our approach provides a more effective way to model people interactions in a global context. Our approach achieves performance comparable to state of the art on two challenging datasets for people and pedestrian detection.