A new algorithm to rigid and non-rigid object tracking in complex environments

Mazinan, A. H.; Amir-Latifi, A.

doi:10.1007/s00170-012-4129-9

Cited by 11 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MS was also combined with other tracking methods but the combination with Kalman Filters (KF) was the most popular [39], [40]. The results were improved especially the number of iterations are reduced by accelerating the convergence and the tracker was able to track under full occlusion as well.…”

Section: State-of-the-art Methodsmentioning

confidence: 99%

Integration of Mean-Shift and Particle Filter: A Survey

Khattak¹,

Raja

Anjum

et al. 2014

2014 12th International Conference on Frontiers of Information Technology

View full text Add to dashboard Cite

Object tracking has become the cornerstone of many computer vision applications. Numerous object tracking methods have surfaced in the research community which are intended for high level applications such as automatic data analysis for activity recognition. Most of the methods are either too constrained in the context of the given application or they are costly in terms of computations to meet the real-time requirements. For example, Mean-Shift (MS) has rose to prominence due to its ease of implementation and robustness to various deformations however it fails to track objects with small sizes, fast motion and full occlusion. On the other hand, Particle Filter (PF) is known for its efficiency, accuracy and robustness to small sizes, fast object motion and full occlusion however it is heavily influenced by the number of particles, besides the sample degeneracy and impoverishment problems. Decoupling the disadvantages of both the methods gave birth to a new era of modern trackers known as hybrid systems that are more efficient, accurate and robust to the aforementioned constraints.A few survey papers on object tracking has been published in the scientific circles during the last decade however we feel that this popular integration of MS and PF is still unregistered.

show abstract

Section: State-of-the-art Methodsmentioning

confidence: 99%

Integration of Mean-Shift and Particle Filter: A Survey

Khattak¹,

Raja

Anjum

et al. 2014

2014 12th International Conference on Frontiers of Information Technology

View full text Add to dashboard Cite

show abstract

“…When there is a partial or full occlusion, the Bhattacharyya coefficient drops [15]. Therefore, one of the best methods for detecting this error of tracking is studying changes of the Bhattacharyya coefficient during the tracking.…”

Section: Tracking Framework Of Combining Cbwh Mean Shfit With Ukfmentioning

confidence: 99%

A robust framework for object tracking based on corrected background-weighted histogram mean shift and unscented Kalman filter

Ahmed

Ali

Hadhoud

2014

International Image Processing, Applications and Systems Conference

View full text Add to dashboard Cite

tracking objects under the presence of noise, objects with partial and full occlusions in complex environments is a challenge for classical mean shift and unscented Kalman filter algorithms. In this paper we propose a new algorithm combining mean shift algorithm with corrected backgroundweighted histogram (CBWH) and unscented Kalman filter (UKF). The CBWH scheme can effectively reduce background's interference in target localization. So CBWH can guarantee accurate localization of the target. Then UKF algorithm has the ability to estimate the coming state. So the proposed algorithm is used to enhance the solution of object tracking problems. The experimental results show that the proposed method is superior to the traditional tracking methods.

show abstract

“…The Tracking Algorithm Based on MS with CBWH and KF. When there is a partial or full occlusion, the Bhattacharyya coefficient drops [11]. Therefore, one of the best methods for detecting this error of tracking is studying changes of the Bhattacharyya coefficient during the tracking.…”

Section: Advanced Information and Computer Technology In Engineering ...mentioning

confidence: 99%

Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

Yang

Jia

Rong

et al. 2013

AMR

View full text Add to dashboard Cite

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

show abstract

A new algorithm to rigid and non-rigid object tracking in complex environments

Cited by 11 publications

References 13 publications

Integration of Mean-Shift and Particle Filter: A Survey

Integration of Mean-Shift and Particle Filter: A Survey

A robust framework for object tracking based on corrected background-weighted histogram mean shift and unscented Kalman filter

Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

Contact Info

Product

Resources

About