Full metadata
Title
Efficient implementation of a low cost object tracking system
Description
Object tracking is an important topic in multimedia, particularly in applications such as teleconferencing, surveillance and human-computer interface. Its goal is to determine the position of objects in images continuously and reliably. The key steps involved in object tracking are foreground detection to detect moving objects, clustering to enable representation of an object by its centroid, and tracking the centroids to determine the motion parameters.
In this thesis, a low cost object tracking system is implemented on a hardware accelerator that is a warp based processor for SIMD/Vector style computations. First, the different foreground detection techniques are explored to figure out the best technique that involves the least number of computations without compromising on the performance. It is found that the Gaussian Mixture Model proposed by Zivkovic gives the best performance with respect to both accuracy and number of computations. Pixel level parallelization is applied to this algorithm and it is mapped onto the hardware accelerator.
Next, the different clustering algorithms are studied and it is found that while DBSCAN is highly accurate and robust to outliers, it is very computationally intensive. In contrast, K-means is computationally simple, but it requires that the number of means to be specified beforehand. So, a new clustering algorithm is proposed that uses a combination of both DBSCAN and K-means algorithm along with a diagnostic algorithm on K-means to estimate the right number of centroids. The proposed hybrid algorithm is shown to be faster than the DBSCAN algorithm by ~2.5x with minimal loss in accuracy. Also, the 1D Kalman filter is implemented assuming constant acceleration model. Since the computations involved in Kalman filter is just a set of recursive equations, the sequential model in itself exhibits good performance, thereby alleviating the need for parallelization. The tracking performance of the low cost implementation is evaluated against the sequential version. It is found that the proposed hybrid algorithm performs very close to the reference algorithm based on the DBSCAN algorithm.
In this thesis, a low cost object tracking system is implemented on a hardware accelerator that is a warp based processor for SIMD/Vector style computations. First, the different foreground detection techniques are explored to figure out the best technique that involves the least number of computations without compromising on the performance. It is found that the Gaussian Mixture Model proposed by Zivkovic gives the best performance with respect to both accuracy and number of computations. Pixel level parallelization is applied to this algorithm and it is mapped onto the hardware accelerator.
Next, the different clustering algorithms are studied and it is found that while DBSCAN is highly accurate and robust to outliers, it is very computationally intensive. In contrast, K-means is computationally simple, but it requires that the number of means to be specified beforehand. So, a new clustering algorithm is proposed that uses a combination of both DBSCAN and K-means algorithm along with a diagnostic algorithm on K-means to estimate the right number of centroids. The proposed hybrid algorithm is shown to be faster than the DBSCAN algorithm by ~2.5x with minimal loss in accuracy. Also, the 1D Kalman filter is implemented assuming constant acceleration model. Since the computations involved in Kalman filter is just a set of recursive equations, the sequential model in itself exhibits good performance, thereby alleviating the need for parallelization. The tracking performance of the low cost implementation is evaluated against the sequential version. It is found that the proposed hybrid algorithm performs very close to the reference algorithm based on the DBSCAN algorithm.
Date Created
2015
Contributors
- Sasikumar, Asha (Author)
- Chakrabarti, Chaitali (Thesis advisor)
- Ogras, Umit Y. (Committee member)
- Suppapola, Antonia Pappandreau (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 53 pages : illustrations (some color)
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.36403
Statement of Responsibility
by Asha Sasikumar
Description Source
Viewed on February 17, 2016
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2015
Note type
thesis
Includes bibliographical references (pages 50-53)
Note type
bibliography
Field of study: Electrical engineering
System Created
- 2016-02-01 07:02:49
System Modified
- 2021-08-30 01:26:02
- 2 years 7 months ago
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