IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Vehicle Visual Tracking from a Moving Vehicle
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
A Framework for Model-Based Tracking Experiments in Image Sequences
International Journal of Computer Vision
Robust tracking with motion estimation and local Kernel-based color modeling
Image and Vision Computing
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Initialization of Model-Based Vehicle Tracking in Video Sequences of Inner-City Intersections
International Journal of Computer Vision
Application of the Particle Filter to Tracking of Fish in Aquaculture Research
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Particle Filter Based Object Tracking with Sift and Color Feature
ICMV '09 Proceedings of the 2009 Second International Conference on Machine Vision
Unconstrained multiple-people tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Freegaming: Mobile, collaborative, adaptive and augmented exergaming
Mobile Information Systems
Efficient tracking using a robust motion estimation technique
Multimedia Tools and Applications
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For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems.