Multi-target tracking in crowded scenes
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Long term real trajectory reuse through region goal satisfaction
MIG'11 Proceedings of the 4th international conference on Motion in Games
A large margin framework for single camera offline tracking with hybrid cues
Computer Vision and Image Understanding
On collaborative people detection and tracking in complex scenarios
Image and Vision Computing
Automatic estimation of movement statistics of people
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Multiple human tracking in high-density crowds
Image and Vision Computing
State-driven particle filter for multi-person tracking
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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
Group tracking: exploring mutual relations for multiple object tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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
(MP)2T: multiple people multiple parts tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Segmentation based particle filtering for real-time 2d object tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Multi-person tracking-by-detection based on calibrated multi-camera systems
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Robust abandoned object detection integrating wide area visual surveillance and social context
Pattern Recognition Letters
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Monocular pedestrian tracking from a moving vehicle
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Iterative hypothesis testing for multi-object tracking with noisy/missing appearance features
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Engineering Applications of Artificial Intelligence
Inserting virtual pedestrians into pedestrian groups video with behavior consistency
The Visual Computer: International Journal of Computer Graphics
Tracking with a mixed continuous-discrete Conditional Random Field
Computer Vision and Image Understanding
Multi-target tracking on confidence maps: An application to people tracking
Computer Vision and Image Understanding
Multi-robot cooperative spherical-object tracking in 3D space based on particle filters
Robotics and Autonomous Systems
A comparative study on multi-person tracking using overlapping cameras
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Pattern Recognition Letters
Macrofeature layout selection for pedestrian localization and its acceleration using GPU
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
| Hi-index | 0.14 |
In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multiperson tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online-trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multiperson tracking. The algorithm detects and tracks a large number of dynamically moving people in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness.