Energy saving in joint acoustic-video target detection and tracking

Shuqun Zhang; Galathara Ananda Kahanda

doi:10.1117/12.723461

11 May 2007 Energy saving in joint acoustic-video target detection and tracking

Shuqun Zhang, Galathara Ananda Kahanda

Proceedings Volume 6562, Unattended Ground, Sea, and Air Sensor Technologies and Applications IX; 65620G (2007) https://doi.org/10.1117/12.723461
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Fusing acoustic and visual data has the potential to significantly improve target tracking performance by exploiting the complementary and redundant information. However, it is not clear how to efficiently fuse these two modalities and how to reduce the high energy consumption of video sensors to improve network lifespan. High energy requirement of video sensors is mainly due to the high sample acquisition cost and high computational complexity of target detection algorithms. We address the computation energy by performing target detection in a region-ofinterest and studying target detection algorithms' computational complexity and data fusion architecture. Some strategies are proposed to help video sensors in performing target detection and reducing the power consumption by incorporating the acoustic detection result. We study two widely used target detection algorithms, background subtraction and snake, for their use in joint acoustic-video tracking, and analyze their computational complexities and memory requirements. We hope that the analysis can effectively guide the designer to select the most suitable algorithm and architecture for a given scenario or application.

Citation Download Citation

Shuqun Zhang and Galathara Ananda Kahanda "Energy saving in joint acoustic-video target detection and tracking", Proc. SPIE 6562, Unattended Ground, Sea, and Air Sensor Technologies and Applications IX, 65620G (11 May 2007); https://doi.org/10.1117/12.723461

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KEYWORDS

Sensors

Video

Acoustics

Fusion energy

Target detection

Data fusion

Detection and tracking algorithms

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