Combining Intensity and Motion for Incremental Segmentation and Tracking Over Long Image Sequences
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Object Tracking in Cluttered Background Based on Optical Flows and Edges
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A feature-based tracking algorithm for vehicles in intersections
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Pattern Recognition
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We present a method to identify the trajectories of moving vehicles from various viewpoints using manifold learning to be implemented on an embedded platform for traffic surveillance. We use a robust kernel Isomap to estimate the intrinsic low-dimensional manifold of input space. During training, the extracted features of the training data are projected on to a 2D manifold and features corresponding to each trajectory are clustered in to k clusters, each represented as a Gaussian model. During identification, features of test data are projected on to the 2D manifold constructed during training and the Mahalanobis distance between test data and Gaussian models of each trajectory is evaluated to identify the trajectory. Experimental results demonstrate the effectiveness of the proposed method in estimating the trajectories of the moving vehicles, even though shapes and sizes of vehicles change rapidly.