GMCP-Tracker: global multi-object tracking using generalized minimum clique graphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
(MP)2T: multiple people multiple parts tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Exploiting pedestrian interaction via global optimization and social behaviors
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
A unified framework for multi-target tracking and collective activity recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Tracking with a mixed continuous-discrete Conditional Random Field
Computer Vision and Image Understanding
Tracking in dense crowds using prominence and neighborhood motion concurrence
Image and Vision Computing
Learning intentions for improved human motion prediction
Robotics and Autonomous Systems
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We propose an agent-based behavioral model of pedestrians to improve tracking performance in realistic scenarios. In this model, we view pedestrians as decision-making agents who consider a plethora of personal, social, and environmental factors to decide where to go next. We formulate prediction of pedestrian behavior as an energy minimization on this model. Two of our main contributions are simple, yet effective estimates of pedestrian destination and social relationships (groups). Our final contribution is to incorporate these hidden properties into an energy formulation that results in accurate behavioral prediction. We evaluate both our estimates of destination and grouping, as well as our accuracy at prediction and tracking against state of the art behavioral model and show improvements, especially in the challenging observational situation of infrequent appearance observations-something that might occur in thousands of webcams available on the Internet.