Measuring Particles in Joint Feature-Spatial Space
スポンサーリンク
概要
- 論文の詳細を見る
Particle filter has attracted increasing attention from researchers of object tracking due to its promising property of handling nonlinear and non-Gaussian systems. In this paper, we mainly explore the problem of precisely estimating observation likelihoods of particles in the joint feature-spatial space. For this purpose, a mixture Gaussian kernel function based similarity is presented to evaluate the discrepancy between the target region and the particle region. Such a similarity can be interpreted as the expectation of the spatial weighted feature distribution over the target region. To adapt outburst of object motion, we also present a method to appropriately adjust state transition model by utilizing the priors of motion speed and object size. In comparison with the standard particle filter tracker, our tracking algorithm shows the better performance on challenging video sequences.
- （社）電子情報通信学会の論文
- 2009-07-01
著者
-
WANG Guijin
Department of Electronic Engineering, Tsinghua University
-
Wang Guijin
Department Of Electronics Engineering Tsinghua University
-
Lin Xinggang
Department Of Electronic Engineering Tsinghua University
-
Lin Xinggang
Department Of Electronics Engineering Tsinghua University
-
Sha Liang
Department Of Electronics Engineering Tsinghua University
-
YAO Anbang
Department of Electronic Engineering, Tsinghua University
-
Yao Anbang
Department Of Electronic Engineering Tsinghua University
-
Wang Guijin
Department Of Electronic Engineering Tsinghua University
関連論文
- Online HOG Method in Pedestrian Tracking
- Robust Object Tracking via Combining Observation Models
- A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction
- Measuring Particles in Joint Feature-Spatial Space
- Kernel Based Image Registration Incorporating with Both Feature and Intensity Matching
- Real-Time Human Detection Using Hierarchical HOG Matrices
- DSP-Based Parallel Implementation of Speeded-Up Robust Features
- Non-rigid Object Tracking as Salient Region Segmentation and Association
- Partial Derivative Guidance for Weak Classifier Mining in Pedestrian Detection
- Drastic Anomaly Detection in Video Using Motion Direction Statistics
- High-Accuracy Sub-Pixel Registration for Noisy Images Based on Phase Correlation
- Stereo Matching Using Local Plane Fitting in Confidence-Based Support Window
- Implementation of Scale and Rotation Invariant On-Line Object Tracking Based on CUDA
- A Real-Time Human Detection System for Video
- An Interleaving Updating Framework of Disparity and Confidence Map for Stereo Matching
- Kernel-Based On-Line Object Tracking Combining both Local Description and Global Representation