Human Tracking by IP PTZ Camera Control in the Context of Video Surveillance
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Hybrid tracking approach for assistive environments
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Graph-based transductive learning for robust visual tracking
Pattern Recognition
Fuzzy Feature-Based Upper Body Tracking with IP PTZ Camera Control
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
An incremental Bhattacharyya dissimilarity measure for particle filtering
Pattern Recognition
Object tracking based on the combination of learning and cascade particle filter
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Cascade particle filter for human tracking with multiple and heterogeneous cameras
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Survey on contemporary remote surveillance systems for public safety
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Joint multitarget object tracking and interaction analysis by a probabilistic bio-inspired model
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Face tracking and recognition considering the camera's field of view
HBU'10 Proceedings of the First international conference on Human behavior understanding
Human tracking using convolutional neural networks
IEEE Transactions on Neural Networks
Video-object segmentation and 3D-trajectory estimation for monocular video sequences
Image and Vision Computing
Efficient visual object tracking with online nearest neighbor classifier
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Robust auxiliary particle filter with an adaptive appearance model for visual tracking
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Recent advances and trends in visual tracking: A review
Neurocomputing
Robust object tracking for resource-limited hardware systems
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
Robust real time face tracking in mobile devices
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Real-time visual tracking based on an appearance model and a motion mode
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Multiple-Cue-Based visual object contour tracking with incremental learning
Transactions on Edutainment IX
A novel particle filter with implicit dynamic model for irregular motion tracking
Machine Vision and Applications
Abrupt motion tracking using a visual saliency embedded particle filter
Pattern Recognition
Co-trained generative and discriminative trackers with cascade particle filter
Computer Vision and Image Understanding
Expert Systems with Applications: An International Journal
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Tracking object in low frame rate video or with abrupt motion poses two main difficulties which most conventional tracking methods can hardly handle: 1) poor motion continuity and increased search space; 2) fast appearance variation of target and more background clutter due to increased search space. In this paper, we address the problem from a view which integrates conventional tracking and detection, and present a temporal probabilistic combination of discriminative observers of different lifespans. Each observer is learned from different ranges of samples, with different subsets of features, to achieve varying level of discriminative power at varying cost. An efficient fusion and temporal inference is then done by a cascade particle filter which consists of multiple stages of importance sampling. Experiments show significantly improved accuracy of the proposed approach in comparison with existing tracking methods, under the condition of low frame rate data and abrupt motion of both target and camera.