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.
Highlights
Distributed sensor networks for tracking the movement of people and vehicles in wilderness environments are of great relevance to border patrol applications
The high cost of such specialized sensor nodes limits the scale at which they can be deployed and poses significant risk to soldiers or security personnel who need to periodically maintain or move the nodes. We address these challenges by developing a novel tracking system that operates on commodity mobile devices, on Android-based tablet platforms
6 Conclusions In this paper, we have presented the design and implementation of an adaptive system for detecting and tracking human footsteps and vehicles from mobile devices. Such a mobile-device-based system is motivated by important uses in the prototyping, testing, and demonstration of disposable sensor nodes that are targeted for deployment in border security and other kinds of outdoor intrusion detection applications
Summary
Distributed sensor networks for tracking the movement of people and vehicles in wilderness environments are of great relevance to border patrol applications (e.g., see [1]). Conventional methods for deploying such sensor networks involve the use of complex, specialized, and expensive sensor node platforms. The specialized nature of such platforms leads to significant development and verification time, which slows down our ability to leverage the latest advances in hardware and software technologies. The high cost of such specialized sensor nodes limits the scale at which they can be deployed and poses significant risk to soldiers or security personnel who need to periodically maintain or move the nodes. We address these challenges by developing a novel tracking system that operates on commodity mobile devices, on Android-based tablet platforms. Mobile devices are attractive for use as prototypes
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