Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Accuracy of the Computation of Optical Flow and of the Recovery of Motion Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fast anisotropic Gauss filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
Generation of synthetic image datasets for time-lapse fluorescence microscopy
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
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A diversity of tracking problems exists in which cohorts of densely packed particles move in an organized fashion, however the stability of individual particles within the cohort is low. Moreover, the flows of cohorts can regionally overlap. Together, these conditions yield a complex tracking scenario that cannot be addressed by optical flow techniques that assume piecewise coherent flows, or by multi-particle tracking techniques that suffer from the local ambiguity in particle assignment. Here, we propose a graph-based assignment of particles in three consecutive frames to recover from image sequences the instantaneous organized motion of groups of particles, i.e. flows. The algorithm makes no a priori assumptions on the fraction of particles participating in organized movement, as this number continuously alters with the evolution of the flow fields in time. Graph-based assignment methods generally maximize the number of acceptable particles assignments between consecutive frames and only then minimize the association cost. In dense and unstable particle flow fields this approach produces many false positives. The here proposed approach avoids this via solution of a multi-objective optimization problem in which the number of assignments is maximized while their total association cost is minimized. The method is validated on standard benchmark data for particle tracking. In addition, we demonstrate its application to live cell microscopy where several large molecular populations with different behaviors are tracked.