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Adaptive tracking of people and vehicles using mobile platforms

Research output: Contribution to journalArticleScientificpeer-review

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Adaptive tracking of people and vehicles using mobile platforms. / Ben Salem, Haifa; Damarla, Thyagaraju; Sudusinghe, Kishan; Stechele, Walter; Bhattacharyya, Shuvra S.

In: Eurasip Journal on Advances in Signal Processing, Vol. 2016, No. 1, 65, 01.12.2016.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Ben Salem, H, Damarla, T, Sudusinghe, K, Stechele, W & Bhattacharyya, SS 2016, 'Adaptive tracking of people and vehicles using mobile platforms', Eurasip Journal on Advances in Signal Processing, vol. 2016, no. 1, 65. https://doi.org/10.1186/s13634-016-0356-9

APA

Ben Salem, H., Damarla, T., Sudusinghe, K., Stechele, W., & Bhattacharyya, S. S. (2016). Adaptive tracking of people and vehicles using mobile platforms. Eurasip Journal on Advances in Signal Processing, 2016(1), [65]. https://doi.org/10.1186/s13634-016-0356-9

Vancouver

Ben Salem H, Damarla T, Sudusinghe K, Stechele W, Bhattacharyya SS. Adaptive tracking of people and vehicles using mobile platforms. Eurasip Journal on Advances in Signal Processing. 2016 Dec 1;2016(1). 65. https://doi.org/10.1186/s13634-016-0356-9

Author

Ben Salem, Haifa ; Damarla, Thyagaraju ; Sudusinghe, Kishan ; Stechele, Walter ; Bhattacharyya, Shuvra S. / Adaptive tracking of people and vehicles using mobile platforms. In: Eurasip Journal on Advances in Signal Processing. 2016 ; Vol. 2016, No. 1.

Bibtex - Download

@article{1869441ae5d44ebfaf2692e707e40e7a,
title = "Adaptive tracking of people and vehicles using mobile platforms",
abstract = "Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost- and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.",
keywords = "Acoustic sensors, Dataflow graphs, DDDAS, Mobile platforms, Signal processing systems, Target tracking",
author = "{Ben Salem}, Haifa and Thyagaraju Damarla and Kishan Sudusinghe and Walter Stechele and Bhattacharyya, {Shuvra S.}",
year = "2016",
month = "12",
day = "1",
doi = "10.1186/s13634-016-0356-9",
language = "English",
volume = "2016",
journal = "Eurasip Journal on Advances in Signal Processing",
issn = "1687-6172",
publisher = "Springer International Publishing AG",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Adaptive tracking of people and vehicles using mobile platforms

AU - Ben Salem, Haifa

AU - Damarla, Thyagaraju

AU - Sudusinghe, Kishan

AU - Stechele, Walter

AU - Bhattacharyya, Shuvra S.

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost- and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.

AB - Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost- and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.

KW - Acoustic sensors

KW - Dataflow graphs

KW - DDDAS

KW - Mobile platforms

KW - Signal processing systems

KW - Target tracking

U2 - 10.1186/s13634-016-0356-9

DO - 10.1186/s13634-016-0356-9

M3 - Article

VL - 2016

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

SN - 1687-6172

IS - 1

M1 - 65

ER -