Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of human groups in videos
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Efficient use of geometric constraints for sliding-window object detection in video
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
To track or to detect? an ensemble framework for optimal selection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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-by-detection of multiple persons by a resample-move particle filter
Machine Vision and Applications
Pattern Recognition Letters
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Tracking multiple objects is important in many application domains. We propose a novel algorithm for multi-object tracking that is capable of working under very challenging conditions such as minimal hardware equipment, uncalibrated monocular camera, occlusions and severe background clutter. To address this problem we propose a new method that jointly estimates object tracks, estimates corresponding 2D/3D temporal trajectories in the camera reference system as well as estimates the model parameters (pose, focal length, etc) within a coherent probabilistic formulation. Since our goal is to estimate stable and robust tracks that can be univocally associated to the object IDs, we propose to include in our formulation an interaction (attraction and repulsion) model that is able to model multiple 2D/3D trajectories in space-time and handle situations where objects occlude each other. We use a MCMC particle filtering algorithm for parameter inference and propose a solution that enables accurate and efficient tracking and camera model estimation. Qualitative and quantitative experimental results obtained using our own dataset and the publicly available ETH dataset shows very promising tracking and camera estimation results.