Efficient algorithms for finding maximum matching in graphs
ACM Computing Surveys (CSUR)
Integrated Person Tracking Using Stereo, Color, and Pattern Detection
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4S: A real-time system detecting and tracking people in 2 1/2D
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Motion-Based Recognition of Pedestrians
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Simultaneous Estimation of Segmentation and Shape
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian detection in uncontrolled environments using stereo and biometric information
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiple-person tracker with a fixed slanting stereo camera
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Pedestrian detection using stereo and biometric information
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
People detection and tracking through stereo vision for human-robot interaction
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
Ambient Intelligence: A New Multidisciplinary Paradigm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Local empirical templates and density ratios for people counting
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Multi-cue-based crowd segmentation in stereo vision
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Scene Aware Detection and Block Assignment Tracking in crowded scenes
Image and Vision Computing
Boosted translation-tolerable classifiers for fast object detection
Image and Vision Computing
Collecting pedestrian trajectories
Neurocomputing
| Hi-index | 0.00 |
In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan-view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases.