Robust Visual Tracking Using Particle Filtering on SL(3) Group

Visual object tracking methods based on wireless multimedia sensor network is one of the research hotspots while the present linear method for processing feature vectors often lead to the tracking drift when tracking object with significant nonplanar pose variations through wireless sensor networks. In this article, we propose a novel nonlinear algorithm for tracking significant deformable objects. The proposed tracking scheme has two filters. On one hand, considering that Grassmann manifold is one of entropy manifold in Lie group manifold, which can describe and process the data of appearance feature more accurately, one filter is designed on it, to estimate the object appearance, by making full use of the transformation relationship between the point on manifold and its corresponding point on tangent space. On the other hand, considering that the process of objects imaging is essentially projection transformation process, the other filter is designed on projection transformation (SL(3)) group, describing the geometric deformation of the objects. The two filters execute alternatively to mitigate tracking drift. Extensive experiments prove that the proposed method can realize stable and accurate tracking for targets with significant geometric deformation, even obscured and illumination changes.

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A Robust Visual Tracking Approach with Adaptive Particle Filtering

2010 Second International Conference on Communication Software and Networks ◽

10.1109/iccsn.2010.66 ◽

2010 ◽

Cited By ~ 2

Author(s):

Tao Xi ◽

Shengxiu Zhang ◽

Shiyuan Yan

Keyword(s):

Visual Tracking ◽

Particle Filtering

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An improved real-time visual tracking method for space non-cooperative target

10.1117/12.2247306 ◽

2016 ◽

Author(s):

Limin Zhang ◽

Feng Zhu ◽

Yingming Hao

Keyword(s):

Real Time ◽

Visual Tracking ◽

Tracking Method

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Applying Dynamic Model for Multiple Manoeuvring Target Tracking using Particle Filtering

International Journal of Information Technology Control and Automation ◽

10.5121/ijitca.2012.2404 ◽

2012 ◽

Vol 2 (4) ◽

pp. 37-49 ◽

Cited By ~ 1

Author(s):

Mohammad Javad Parseh

Keyword(s):

Target Tracking ◽

Dynamic Model ◽

Particle Filtering

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Visual Tracking Using Multimodal Particle Filter

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch044 ◽

2018 ◽

pp. 1072-1090 ◽

Cited By ~ 1

Author(s):

Tony Tung ◽

Takashi Matsuyama

Keyword(s):

Visual Tracking ◽

Particle Filtering ◽

Body Parts ◽

Detection And Tracking ◽

Flow Features ◽

Target Templates ◽

Textured Models ◽

Online Mechanism ◽

Changes Over Time ◽

Appearance Changes

Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body parts from multiple views). Particularly, the authors integrate various cues such as color, motion and depth in a global formulation. The Earth Mover distance is used to compare color models in a global fashion, and constraints on motion flow features prevent common drifting effects due to error propagation. In addition, the model features an online mechanism that adaptively updates a subspace of multimodal templates to cope with appearance changes. Furthermore, the proposed model is integrated in a practical detection and tracking process, and multiple instances can run in real-time. Experimental results are obtained on challenging real-world videos with poorly textured models and arbitrary non-linear motions.

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Visual Tracking Using Multimodal Particle Filter

International Journal of Natural Computing Research ◽

10.4018/ijncr.2014070104 ◽

2014 ◽

Vol 4 (3) ◽

pp. 69-84 ◽

Cited By ~ 1

Author(s):

Tony Tung ◽

Takashi Matsuyama

Keyword(s):

Visual Tracking ◽

Particle Filtering ◽

Body Parts ◽

Tracking Process ◽

Global Formulation ◽

Flow Features ◽

Target Templates ◽

Textured Models ◽

Model Features ◽

Appearance Changes

Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body parts from multiple views). Particularly, the authors integrate various cues such as color, motion and depth in a global formulation. The Earth Mover distance is used to compare color models in a global fashion, and constraints on motion flow features prevent common drifting effects due to error propagation. In addition, the model features an online mechanism that adaptively updates a subspace of multimodal templates to cope with appearance changes. Furthermore, the proposed model is integrated in a practical detection and tracking process, and multiple instances can run in real-time. Experimental results are obtained on challenging real-world videos with poorly textured models and arbitrary non-linear motions.

Download Full-text