Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Detection and Tracking of Moving Vehicles in Crowded Scenes
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Linear Time Maximally Stable Extremal Regions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Multi-cue Based Visual Tracking in Clutter Scenes with Occlusions
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Feature clustering for vehicle detection and tracking in road traffic surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An online learned CRF model for multi-target tracking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we propose a hierarchical feature grouping method for multiple object segmentation and tracking. The proposed method aims to segment and track objects in the object-level without prior knowledge about the scene and object. We firstly group the motion feature into region-level with the proposed region features which represent a homogeneous region in an object. Object-level groups are achieved by clustering the region-level groups based on foreground information and motion similarity. To find optimal object-level groups, we formulate energy minimization problem, design its objective functions and solve it using simulated annealing(SA). By this hiearchical feature grouping, the proposed method efficiently segments and tracks various kinds of objects without object detector, 3D model and geometry information. Experimental results on several video clips show that our approach robustly segments and tracks multiple object regardness of camera position and object-class.