Object tracking in image sequences based on parametric features

1999 ◽  
Vol 116 (6) ◽  
pp. 390-394
Author(s):  
R. Hoischen ◽  
B. Mertsching ◽  
S. Springmann
Keyword(s):  
Object Tracking ◽  
Image Sequences

Object tracking using plenoptic image sequences

2017 ◽  
Author(s):  
Jae Woo Kim ◽  
Seong-Joon Bae ◽  
Seongjin Park ◽  
Do Hyung Kim
Keyword(s):  
Object Tracking ◽  
Image Sequences

scholarly journals DYNAMIC KEYPOINT-BASED ALGORITHM OF OBJECT TRACKING

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 79-82
Author(s):  
A. I. Morgacheva ◽  
V. A. Kulikov ◽  
V. P. Kosykh
Keyword(s):  
Object Tracking ◽  
Image Sequences ◽  
The Other ◽  
Time Varying ◽  
Test Set ◽  
Rule Based ◽  
Original Algorithm ◽  
Function Of Rival Similarity ◽  
Angle Of View ◽  
Dynamic Time

The model of the observed object plays the key role in the task of object tracking. Models as a set of image parts, in particular, keypoints, is more resistant to the changes in shape, texture, angle of view, because local changes apply only to specific parts of the object. On the other hand, any model requires updating as the appearance of the object changes with respect to the camera. In this paper, we propose a dynamic (time-varying) model, based on a set of keypoints. To update the data this model uses the algorithm of rating keypoints and the decision rule, based on a Function of Rival Similarity (FRiS). As a result, at the test set of image sequences the improvement was achieved on average by 9.3% compared to the original algorithm. On some sequences, the improvement was 16% compared to the original algorithm.

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.‎

scholarly journals Object Tracking via 2DPCA andl2-Regularization

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Haijun Wang ◽  
Hongjuan Ge ◽  
Shengyan Zhang
Keyword(s):  
Bayesian Inference ◽  
Object Tracking ◽  
Likelihood Function ◽  
State Of The Art ◽  
Reconstruction Error ◽  
Image Sequences ◽  
Projection Matrix ◽  
Tracking Method ◽  
Fast Tracking ◽  
Bayesian Inference Framework

We present a fast and robust object tracking algorithm by using 2DPCA andl2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt thel2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.

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