Motion tracking system in video based on extensive feature set

Uke, Nilesh; Thool, Ravindra C.

doi:10.1179/1743131x13y.0000000052

Cited by 7 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1,2 One of the most popular techniques is the mean-shift algorithm due to its robustness, ease of implementation and computational efficiency. However, the standard mean-shift algorithm suffers from a number of problems which adversely affect tracking performance.…”

Section: Introductionmentioning

confidence: 99%

A multimodal approach for individual tracking of people and their belongings

Beyan

Temizel

2015

The Imaging Science Journal

View full text Add to dashboard Cite

In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object's size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.

show abstract

Section: Introductionmentioning

confidence: 99%

A multimodal approach for individual tracking of people and their belongings

Beyan

Temizel

2015

The Imaging Science Journal

View full text Add to dashboard Cite

show abstract

“…1 The past few decades have witnessed the remarkable development of this topic in computer vision and its applications.…”

Section: Introductionmentioning

confidence: 99%

Time domain optimisation for monocular visual navigation: moving horizon approach

Liao

Yang

Liu

et al. 2015

The Imaging Science Journal

View full text Add to dashboard Cite

Estimating ego motion of monocular visual system from the input image sequence is a critical problem in computer vision. This paper considers this problem from the view of time domain optimisation. At first, based on the frame to frame matches, the traditional monocular visual navigation algorithm is introduced, and this process is also seen as a two-view method. On the basis of the three-view matches, the trifocal tensor based monocular navigation algorithm is described. In addition, this paper proposes a novel moving horizon estimation (MHE) based algorithm to tackle the pose estimation problem. In this algorithm, the epipolar constraints and the closed loop constraints of position along with consecutive time steps are all involved in the global optimisation model. In accordance with this optimisation function, the MHE is introduced to achieve the tradeoff between computation costs and estimation accuracy. Because of the inherent robustness of the referred moving horizon estimator, it is becoming more popular in various applications. Based on the general epipolar constraints, trifocal tensor constraints and step crossed optimisation function during the whole moving window, the corresponding three referred pose estimation algorithms are all implemented comparatively at the end of this paper. The rotation and translation experiments are implemented to validate the improvements of the time domain optimisation methods.List of symbols C k camera optical centered coordinate system in kth step I k image coordinate system in kth step q ab rotation quaternion from a to b system r ab relative displacement vector between a and b system r abc , V abc , v abc description of r abc , V abc and v abc in c system R ab direction cosine matrix between a and b system V ab relative velocity vector between a and b system, m s 21 W world coordinate system v ab relative angular velocity between a and b system, rad s 21 P C k optical centered plane, xoy plane of C k P I k image plane, xoy plane of I k

show abstract

Automatic Helmet (Object) Detection and Tracking the Riders Using Kalman Filter Technique

Vairamani

2023

Contributions to Environmental Sciences &Amp; Innovative Business Technology

View full text Add to dashboard Cite

Motion tracking system in video based on extensive feature set

Cited by 7 publications

References 26 publications

A multimodal approach for individual tracking of people and their belongings

A multimodal approach for individual tracking of people and their belongings

Time domain optimisation for monocular visual navigation: moving horizon approach

Automatic Helmet (Object) Detection and Tracking the Riders Using Kalman Filter Technique

Contact Info

Product

Resources

About