Thiago Teixeira

Lightweight People Counting and Localizing in Indoor Spaces Using Camera Sensor Nodes

This paper presents a lightweight method for localizing and counting people in indoor spaces using motion and size criteria. A histogram designed to filter moving objects within a specified size range, can operate directly on frame difference output to localize human-sized moving entities in the field of view of each camera node. Our method targets a custom, ultra-low power imager architecture operating on address-event representation, aiming to implement the proposed algorithm on silicon. In this paper we describe the details of our design and experimentally determine suitable parameters for the proposed histogram. The resulting histogram and counting algorithm are implemented and tested on a set of iMote2 camera sensor nodes deployed in our lab.

Sensor node lifetime analysis: Models and tools

This article presents two lifetime models that describe two of the most common modes of operation of sensor nodes today, trigger-driven and duty-cycle driven. The models use a set of hardware parameters such as power consumption per task, state transition overheads, and communication cost to compute a nodes average lifetime for a given event arrival rate. Through comparison of the two models and a case study from a real camera sensor node design we show how the models can be applied to drive architectural decisions, compute energy budgets and duty-cycles, and to preform side-by-side comparison of different platforms. Based on our models we present a MATLAB Wireless Sensor Node Platform Lifetime Prediction and Simulation Package (MATSNL). This demonstrates the use of the models using sample applications drawn from existing sensor node measurements.

The BehaviorScope framework for enabling ambient assisted living

The in-house monitoring of elders using intelligent sensors is a very desirable service that has the potential of increasing autonomy and independence while minimizing the risks of living alone. Because of this promise, the efforts of building such systems have been spanning for decades, but there is still a lot of room for improvement. Driven by the recent technology advances in many of the required components, in this article, we present a scalable framework for detailed behavior interpretation. Our framework supports in-house monitoring of elders using an intelligent gateway and a set of cheap commercially available sensors, in addition to more advanced camera-based human localization sensors and a client for GPS-enabled mobile phones that provides monitoring when outdoors. In this article, we report our experiences and present our current progress in three main components: sensors, middleware, and behavior interpretation mechanisms spanning from simple programmable rule-based alerts to algorithms for extracting the temporal routines of individuals.

Address-event imagers for sensor networks: evaluation and modeling

Although imaging is an information-rich sensing modality, the use of cameras in sensor networks is very often prohibited by factors such as power, computation cost, storage, communication bandwidth and privacy. In this paper we consider information selective and privacy-preserving address-event imagers for sensor networks. Instead of providing full images with a high degree of redundancy, our efforts in the design of these imagers specialize on selecting a handful of features from a scene and outputting these features in address-event representation. In this paper we present our initial results in modeling and evaluating address-event sensors in the context of sensor networks. Using three different platforms that we have developed, we illustrate how to model address-event cameras and how to build an emulator using these models. We also present a lightweight classification scheme to illustrate the computational advantages of address-event sensors. The paper concludes with an evaluation of the classification algorithm and a feasibility study of using COTS components to emulate address-event inside a sensor network

Model-based design exploration of wireless sensor node lifetimes

This paper presents two lifetime models that describe two of the most common modes of operation of sensor nodes today, trigger-driven and duty-cycle driven. The models use a set of hardware parameters such as power consumption per task, state transition overheads, and communication cost to compute a nodes average lifetime for a given event arrival rate. Through comparison of the two models and a case study from a real camera sensor node design we show how the models can be applied to drive architectural decisions, compute energy budgets and duty-cycles, and to preform side-by-side comparison of different platforms.

BehaviorScope: Real-Time Remote Human Monitoring Using Sensor Networks

In this demonstration we present the BehaviorScope, a system for interpreting human activity patterns using a sensor network and its application to elder monitoring in assisted living. The BehaviorScope provides a runtime, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. Human activities are described in high-level scripts that are directly mapped to a hierarchy of probabilistic grammars that parse low-level sensor measurements into high-level distinguishable activities. Activities of interest are pre-programmed into a specification that is used by the system to interpret the incoming sensor data stream. The system interprets the activities to generate summaries and other triggered notifications that are propagated to stakeholders via email and cell-phone text messages.

PEM-ID: Identifying people by gait-matching using cameras and wearable accelerometers

The ability to localize and identify multiple people is paramount to the inference of high-level activities for informed decision-making. In this paper, we describe the PEM-ID system, which uniquely identifies people tagged with accelerometer nodes in the video output of preinstalled infrastructure cameras. For this, we introduce a new distance measure between signals comprised of timestamps of gait landmarks, and utilize it to identify each tracked person from the video by pairing them with a wearable accelerometer node.

Macroscopic Human Behavior Interpretation Using Distributed Imager and Other Sensors

This paper presents BScope, a new system for interpreting human activity patterns using a sensor network. BScope provides a runtime, 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 lenses that observe peoples 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 runtime by using more than 1.3 million location measurements acquired through our real home deployment.

Event-based imaging with active illumination in sensor networks

We discuss a distributed imaging architecture with active illumination for sensor network applications. An event-based CMOS imager is employed at the sensor level, to convert light intensity at each pixel into a pulse density modulated stream of address events. The wireless nodes are commercial off-the-shelf motes. Energy-aware communication is implemented at the sensor level by employing an event-based readout. Additional computation for data reduction is accomplished at the sensor/mote interface level by modulating the event-rate produced by the sensor array to match the bandwidth and latency constraints in the communication network. Information transmitted in the limited bandwidth links of the network yields effective means for detection and partial recognition of the object, even at very low bit rates and frame latency as low as 1s.

Lightweight People Counting and Localizing for Easily Deployable Indoors WSNs

We describe a lightweight method for counting and localizing people using camera sensor networks. The algorithm makes use of a motion histogram to detect people based on motion and size criteria. The motion histogram is an averaged shifted histogram that estimates the distribution of people in a room given the above-threshold pixels in a frame-differenced ldquomotionrdquo image. The algorithm provides good detection rates at low computational complexity. In this paper, we describe the details of our design and experimentally determine suitable parameters for the proposed histogram. The resulting histogram and counting algorithm are implemented and tested on a network of iMote2 sensor nodes. Our implementation on sensor nodes uses a custom sensor board with a commercial off-the-shelf camera, but the motion histogram is designed to easily adapt to ultralow-power address-event motion imagers.