Dynamic Object Tracking Based on KAZE Features and Particle Filter

2014 ◽  
Vol 556-562 ◽  
pp. 2702-2706
Author(s):  
Ying Xia ◽  
Xin Hao Xu
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Scale Invariance ◽  
Experimental Results ◽  
Object Model ◽  
Dynamic Object ◽  
Color Features ◽  
Dynamic Tracking ◽  
Object Appearance ◽  
Accuracy And Stability

Accuracy and stability is crucial for dynamic object tracking. Considering the scale invariance, rotational invariance and strong anti-jamming capability of KAZE features, a method of dynamic object tracking based on KAZE features and particle filter is proposed. This method obtains the global color features of the dynamic object appearance and extracts its local KAZE features to construct the object model first, and then performs dynamic tracking by particle filter. Experimental results demonstrate the accuracy and stability of the proposed method.

A COLOR FEATURES-BASED METHOD FOR OBJECT TRACKING EMPLOYING A PARTICLE FILTER ALGORITHM

2009 ◽  
Author(s):  
Budi Sugandi ◽  
Hyoungseop Kim ◽  
Joo Kooi Tan ◽  
Seiji Ishikawa ◽  
Abdul Halim Hakim ◽  
...  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Color Features ◽  
Particle Filter Algorithm

An Improved Tracking Algorithm Combing Color and LBP Texture Features Based on Particle Filter

2013 ◽  
Vol 385-386 ◽  
pp. 1484-1487
Author(s):  
En Zeng Dong ◽  
Li Ya Su ◽  
Yan Hong Fu
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Reference Model ◽  
Texture Features ◽  
Tracking Algorithm ◽  
Color Histogram ◽  
Experimental Results ◽  
Single Feature ◽  
Highly Effective

In this paper, an tracking algorithm combing color and LBP texture features based on particle filter is proposed to overcome the disadvantages of existing particle filter object tracking methods. A color histogram and a texture histogram were combined to build the objects reference model, effectively improving the accuracy of object tracking. Experimental results demonstrate that, compared with the method based on single feature, the proposed method is highly effective, valid and is practicable.

Particle Filter Object Tracking Algorithm Based on Color and Gradient

2014 ◽  
Vol 926-930 ◽  
pp. 3141-3144 ◽  
Author(s):  
Jiai He ◽  
Yong Na Li
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Target Tracking ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Weighted Fusion ◽  
Illumination Changes ◽  
Particle Filter Algorithm

With the robustness of a single color which is not high in standard particle filter tracking, a fusion of color and gradient particle filter algorithm is proposed. By the advantages of color described the target ’global and gradients described the shape of structure, they are weighted fusion to form a new integrated histogram and applied to the particle filter framework. The experimental results show that compared with the traditional particle filter algorithm, the text of the algorithm can achieve relatively reliable target tracking under complicated background and illumination changes, with better robustness and reliability.

A Novel Object Tracking Based on Foreground Hue Histogram

2013 ◽  
Vol 278-280 ◽  
pp. 1205-1210
Author(s):  
Yun Gao ◽  
Hao Zhou ◽  
Xue Jie Zhang
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Motion Detection ◽  
Foreground Object ◽  
Rigid Object ◽  
Novel Object ◽  
Complex Background ◽  
Object Based ◽  
Object Appearance ◽  
Object Region

We propose a tracking algorithm for a single non-rigid object based on its foreground hue histogram. A tracked region can be described by the foreground hue histogram only calculating foreground object pixels, which can effectively restrain the disturbing of complex background environments. For measuring the object likelihood, we match the foreground hue histogram with that of the tracked object and refer the result of motion detection to encircle the tracked object region as much as possible. During the tracking, we update the hue histogram model for adapting the object appearance variation. The proposed algorithm is realized in the particle filter frame, and the experiments show that it is capable of robustly and accurately tracking a single non-rigid object for the situations of complex background scenes and strong appearance variations.

Enhanced MIL Visual Tracking via Distribution Fields Descriptor

2014 ◽  
Vol 571-572 ◽  
pp. 725-728
Author(s):  
Kang Sun ◽  
Nian Nian Sun
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Experimental Results ◽  
Video Sequences ◽  
Object Model ◽  
Object Appearance ◽  
Distribution Field

This paper presents an enhanced multiple instance learning (MIL) tracker with a suitable representation of object appearance called distribution field descriptor (DF). To address transformations of object template (rotation, scaling), we firstly replace the smoothed histograms used in DF with smoothed bins according the theory of averaged shifted histograms. Secondly, due to the DF specificity and landscape smoothness, we adopt DF descriptor instead of traditional Haar-like one to represent the object appearance. By build object model using selected discriminative layers, our tracker is more robust while needing fewer features than the original tracker. The experimental results show higher performances of our tracker than those of five state-of-the-art ones on several challenging video sequences.

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

2021 ◽  
pp. 1-12
Author(s):  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
High Dimensional ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

2020 ◽  
pp. 2155002
Author(s):  
Xiuhua Hu ◽  
Yuan Chen ◽  
Yan Hui ◽  
Yingyu Liang ◽  
Guiping Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Scale Model ◽  
Correlation Filters ◽  
Motion Feature ◽  
Tracking Method ◽  
Object State ◽  
Multi Scale ◽  
Tracking Process ◽  
Object Appearance ◽  
Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

Sign in / Sign up

Export Citation Format

Share Document