Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion-Based Recognition of Pedestrians
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A feedback-based algorithm for motion analysis with application to object tracking
EURASIP Journal on Applied Signal Processing
Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale 3D scene flow from binocular stereo sequences
Computer Vision and Image Understanding
RK-Means Clustering: K-Means with Reliability
IEICE - Transactions on Information and Systems
Real-Time Image-Based Motion Detection Using Color and Structure
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Motion trajectory reproduction from generalized signature description
Pattern Recognition
Adaptable Neural Networks for Objects' Tracking Re-initialization
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
TerraMax vision at the urban challenge 2007
IEEE Transactions on Intelligent Transportation Systems
Multimedia Tools and Applications
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
International Journal of Computational Vision and Robotics
Vector quantization segmentation for head pose estimation
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Human Activity Recognition Using Gait Pattern
International Journal of Computer Vision and Image Processing
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In this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. The application background is vision-based driving assistance in the inner city. In an initial step, object parts are determined by a divisive clustering algorithm, which is applied to all pixels in the first image of the sequence. The feature space is defined by the color and position of a pixel. For each new image the clusters of the previous image are adapted iteratively by a parallel k-means clustering algorithm. Instead of tracking single points, edges, or areas over a sequence of images, only the centroids of the clusters are tracked. The proposed method remarkably simplifies the correspondence problem and also ensures a robust tracking behaviour.