We are family: joint pose estimation of multiple persons
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Multi-person tracking with sparse detection and continuous segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Monocular 3D scene modeling and inference: understanding multi-object traffic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Disparity statistics for pedestrian detection: combining appearance, motion and stereo
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Multi-stage sampling with boosting cascades for pedestrian detection in images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments
International Journal of Robotics Research
Geometrically constrained level set tracking for automotive applications
Proceedings of the 32nd DAGM conference on Pattern recognition
Monocular online learning for road region labeling and object detection from a moving platform
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Efficient use of geometric constraints for sliding-window object detection in video
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Real time vision based multi-person tracking for mobile robotics and intelligent vehicles
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
On collaborative people detection and tracking in complex scenarios
Image and Vision Computing
Online learned discriminative part-based appearance models for multi-human tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Taking mobile multi-object tracking to the next level: people, unknown objects, and carried items
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
3D2PM - 3d deformable part models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Stixels motion estimation without optical flow computation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Fast stixel computation for fast pedestrian detection
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
A survey on multi person identification and localization
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
A data-driven detection optimization framework
Neurocomputing
Robust object tracking in crowd dynamic scenes using explicit stereo depth
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Neurocomputing
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle
Machine Vision and Applications
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In this paper, we address the problem of multiperson tracking in busy pedestrian zones using a stereo rig mounted on a mobile platform. The complexity of the problem calls for an integrated solution that extracts as much visual information as possible and combines it through cognitive feedback cycles. We propose such an approach, which jointly estimates camera position, stereo depth, object detection, and tracking. The interplay between those components is represented by a graphical model. Since the model has to incorporate object-object interactions and temporal links to past frames, direct inference is intractable. We, therefore, propose a two-stage procedure: for each frame, we first solve a simplified version of the model (disregarding interactions and temporal continuity) to estimate the scene geometry and an overcomplete set of object detections. Conditioned on these results, we then address object interactions, tracking, and prediction in a second step. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver robust tracking performance in scenes of realistic complexity.