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.

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.

Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter

2020 ◽  
Vol 10 (2) ◽  
pp. 29-44
Author(s):  
Ibrahim Guelzim ◽  
Amina Amkoui ◽  
Hammadi Nait-Charif
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Classical Method ◽  
Lumbar Vertebrae ◽  
Similarity Measures ◽  
Image Sequences ◽  
Jaccard Index ◽  
Rigid Object ◽  
Automatic Initialization

Vertebrae tracking in videofluoroscopy is a challenging problem because of the low quality ‎of ‎image ‎sequences, like poor image contrast, ambiguous geometry details, and vertebrae rotation. The aim of this article is to tackle this problem by ‎proposing a ‎method for rigid object tracking based on the ‎fragmentation of the tracked object. The proposed method ‎is based on the particle filter using the calculation of the similarity between the ‎respective‏ ‏fragments of ‎objects instead of the whole objects. The similarity measures used are the Jaccard index, the ‎correlation ‎coefficient, and the Bhattacharyya coefficient. The tracking starts with a semi-automatic initialization. ‎The results show that the fragments-based object tracking method outperforms the classical ‎method ‎‎(without fragmentation) for each of the used similarity measures. The results show that the ‎tracking based on the Jaccard index is more stable and outperforms methods based on ‎other similarity ‎measures.‎

PFCHA: A New Moving Object Tracking Algorithms Based on Particle Filter and Histogram

2011 ◽  
Vol 110-116 ◽  
pp. 3343-3350
Author(s):  
Qi Yang ◽  
Jia Fu Jiang
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Target Tracking ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Moving Target ◽  
Trace Detection ◽  
Complex Background ◽  
Motion Characteristics ◽  
The Impact

The complexity of the video background of moving target tracking algorithm led to the robustness of the important reasons is not high for the limitations of existing algorithms, a framework based on the movement of particle filter tracking algorithm. In order to reduce the impact of occlusion for the algorithm, the algorithm of moving objects make full use of color and motion characteristics of moving target detection, and to avoid the interference of the complex background, within the framework of particle filter in the object color histogram analysis. Finally, given an effective comparison of the calculation. Experimental results show that particle filter based target tracking algorithm can effectively remove the interference of the complex background, the context for any trace detection of high robustness.

Reverse Validation Based Adaptive Particle Filter Algorithm for Object Tracking

2014 ◽  
Vol 1044-1045 ◽  
pp. 1302-1308
Author(s):  
Shao Mei Li ◽  
Kai Wang ◽  
Chao Gao ◽  
Ya Wen Wang
Keyword(s):  
Object Tracking ◽  
Particle Filtering ◽  
State Of The Art ◽  
Appearance Model ◽  
Object Based ◽  
Object Appearance ◽  
Sift Features ◽  
Particle Filter Algorithm ◽  
Tracking Object ◽  
Object Frame

To improves tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, tracking object frame by frame via color histogram and particle filtering. Secondly, reversely validating the tracking result based on particle filtering. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm can not only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Tacking Object Based on SIFT Features and Particle Filter

ROBOT ◽  
2010 ◽  
Vol 32 (2) ◽  
pp. 241-247 ◽  
Author(s):  
Changfeng NIU ◽  
Dengfeng CHEN ◽  
Yushu LIU
Keyword(s):  
Particle Filter ◽  
Object Based ◽  
Sift Features

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.

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