Fuzzy chamfer distance and its probabilistic formulation for visual tracking
- Resource Type
- Conference
- Authors
- Yonggang Jin; Mokhtarian, Farzin; Bober, Miroslaw; Illingworth, John
- Source
- 2008 IEEE Conference on Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. :1-8 Jun, 2008
- Subject
- Computing and Processing
Signal Processing and Analysis
Iterative algorithms
High definition video
Particle tracking
Particle filters
Signal processing algorithms
Laboratories
Speech processing
Signal processing
Object recognition
Object detection
- Language
- ISSN
- 1063-6919
The paper presents a fuzzy chamfer distance and its probabilistic formulation for edge-based visual tracking. First, connections of the chamfer distance and the Hausdorff distance with fuzzy objective functions for clustering are shown using a reformulation theorem. A fuzzy chamfer distance (FCD) based on fuzzy objective functions and a probabilistic formulation of the fuzzy chamfer distance (PFCD) based on data association methods are then presented for tracking, which can all be regarded as reformulated fuzzy objective functions and minimized with iterative algorithms. Results on challenging sequences demonstrate the performance of the proposed tracking method.