This paper presents BScope, a new system for interpreting human activity patterns using a sensor network. BScope provides a run-time, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. The users of the system are able to describe human activities with high level scripts that are directly mapped to hierarchical probabilistic grammars used to parse low level sensor measurements into high level distinguishable activities. Our approach is presented, though not limited, in the context of an assisted living application in which a small, privacy preserving camera sensor network of five nodes is used to monitor activity in the entire house over a period of 25 days. Privacy is preserved by the fact that camera sensors only provide discrete high-level features, such as motion information in the form of image locations, and not actual images. In this deployment, our primary sensing modality is a distributed array of image sensors with wide-angle lens that observe people’s locations in the house during the course of the day. We demonstrate that our system can successfully generate summaries of everyday activities and trigger notifications at run-time by using more than 1.3 million location measurements acquired through our real home deployment.

In this paper, we propose and demonstrate a novel wireless camera network system, called CITRIC. The core component of this system is a new hardware platform that integrates a camera, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a camera mote. The design enables in-network processing of images to reduce communication requirements, which has traditionally been high in existing camera networks with centralized processing. We also propose a back-end client/server architecture to provide a user interface to the system and support further centralized processing for higher-level applications. Our camera mote enables a wider variety of distributed pattern recognition applications than traditional platforms because it provides more computing power and tighter integration of physical components while still consuming relatively little power. Furthermore, the mote easily integrates with existing low-bandwidth sensor networks because it can communicate over the IEEE 802.15.4 protocol with other sensor network platforms. We demonstrate our system on three applications: image compression, target tracking, and camera localization.

Abstract—Modern military and civilian surveillance applications should provide end users with the high level representation of events observed by sensors rather than with the raw data measurements. Hence, there is a need for a system that can infer higher level meaning from collected sensor data. We demonstrate that probabilistic context free grammars (PCFGs) can be used as a basis for such a system. To recognize events from raw sensor network measurements, we use a PCFG inference method based on Stolcke(1994) and Chen(1996). We present a fast algorithm for deriving a concise probabilistic context free grammar from the given observational data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We also present a real-world scenario of monitoring a parking lot and the simulation based on this scenario. We described the use of PCFGs to recognize events in the results of such a simulation. We finally demonstrate the deployment details of such an event recognition system. I.

Abstract—In this paper, we describe an address-event vision system designed to detect accidental falls in elderly home care applications. The system raises an alarm when a fall hazard is detected. We use an asynchronous temporal contrast vision sensor which features sub-millisecond temporal resolution. The sensor reports a fall at ten times higher temporal resolution than a frame-based camera and shows 84 % higher bandwidth efficiency as it transmits fall events. A lightweight algorithm computes an instantaneous motion vector and reports fall events. We are able to distinguish fall events from normal human behavior, such as walking, crouching down, and sitting down. Our system is robust to the monitored person’s spatial position in a room and presence of pets. Index Terms—Address-event, AER, assisted living, CMOS image sensor, elderly home care, fall detection, motion detection, temporal-difference, vision sensor. I.

This paper presents the design and implementation of a dual-camera sensor network that can be used as a memory assistant tool for assisted living. Our system performs energy-efficient object detection and recognition of commonly misplaced objects. The novelty in our approach is the ability to tradeoff between recognition accuracy and computational efficiency by employing a combination of low complexity but less precise color histogram-based image recognition together with more complex image recognition using SIFT descriptors. In addition, our system can seamlessly integrate feedback from the user to improve the robustness of object recognition. Experimental results reveal that our system is computation-efficient and adaptive to slow changes of environmental conditions 1.

This paper presents the design and implementation of a dual-camera sensor network that can be used as a memory assistant tool for assisted living. Our system performs energy-efficient object detection and recognition of commonly misplaced objects. The novelty in our approach is the ability to tradeoff between recognition accuracy and computational efficiency by employing a combination of low complexity but less precise color histogram-based image recognition together with more complex image recognition using SIFT descriptors. In addition, our system can seamlessly integrate feedback from the user to improve the robustness of object recognition. Experimental results reveal that our system is computation-efficient and adaptive to slow changes of environmental conditions 1.

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Network testbeds allow the effectiveness of algorithms and protocols to be evaluated by providing a controlled environment for measuring network performance.

Visual sensor networks have emerged as an important class of sensor-based distributed intelligent systems, with unique performance, complexity, and quality of service challenges. Consisting of a large number of low-power camera nodes, visual sensor networks support a great number of novel vision-based applications. The camera nodes provide information from a monitored site, performing distributed and collaborative processing of their collected data. Using multiple cameras in the network provides different views of the scene, which enhances the reliability of the captured events. However, the large amount of image data produced by the cameras combined with the network’s resource constraints require exploring new means for data processing, communication, and sensor management. Meeting these challenges of visual sensor networks requires interdisciplinary approaches, utilizing vision processing, communications and networking, and embedded processing. In this paper, we provide an overview of the current state-of-the-art in the field of visual sensor networks, by exploring several relevant research directions. Our goal is to provide a better understanding of current research problems in the different research fields of visual sensor networks, andtoshowhowthesedifferent research fields should interact to solve the many challenges of visual sensor networks. Copyright © 2009 S. Soro and W. Heinzelman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.

Recent advances in wireless communications technology and low-power, low-cost CMOS imaging sensors stimulate research on the analysis and design of ubiquitous video sensing and delivery in wireless sensor networks. However, scalable deployments remain limited or impractical. Critical challenges such as radio interference, limited channel capacity, and constrained energy resources are still barriers to large-scale deployment of these wireless video sensor networks. The solution space can be explored in several dimensions including data compression, video image analysis and extraction, and intelligent data routing. In this chapter we focus on the analysis of video delivery and data routing techniques for wireless video sensor networks. Our work is intended to inspire additional efforts leading to video routing techniques optimized to different topologies, the physical medium, network channels, and energy constraints.