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Tracking Motion Direction and Distance With Pyroelectric IR Sensors
"... Abstract—Passive IR (PIR) sensors are excellent devices for wireless sensor networks (WSN), being low-cost, low-power, and presenting a small form factor. PIR sensors are widely used as a simple, but reliable, presence trigger for alarms, and automatic lighting systems. However, the output of a PIR ..."
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Abstract—Passive IR (PIR) sensors are excellent devices for wireless sensor networks (WSN), being low-cost, low-power, and presenting a small form factor. PIR sensors are widely used as a simple, but reliable, presence trigger for alarms, and automatic lighting systems. However, the output of a PIR sensor depends on several aspects beyond simple people presence, as, e.g., distance of the body from the sensor, direction of movement, and presence of multiple people. In this paper, we present a feature extraction and sensor fusion technique that exploits a set of wireless nodes equipped with PIR sensors to track people moving in a hallway. Our approach has reduced computational and memory requirements, thus it is well suited for digital systems with limited resources, such as those available in sensor nodes. Using the proposed techniques, we were able to achieve 100 % correct detection of direction of movement and 83.49%–95.35 % correct detection of distance intervals. Index Terms—Classifier, distance, passive IR (PIR), tracking. I.
Multi-modal video surveillance aided by pyroelectric infrared sensors
- in Proc. ECCV Workshop Multi-Camera Multi-Modal Sensor Fusion, Algor. Appl
"... Abstract. The interest in low-cost and small size video surveillance systems able to collaborate in a network has been increasing over the last years. Thanks to the progress in low-power design, research has greatly reduced the size and the power consumption of such distributed embedded systems pro ..."
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Abstract. The interest in low-cost and small size video surveillance systems able to collaborate in a network has been increasing over the last years. Thanks to the progress in low-power design, research has greatly reduced the size and the power consumption of such distributed embedded systems providing flexibility, quick deployment and allowing the implementation of effective vision algorithms performing image processing directly on the embedded node. In this paper we present a multi-modal video sensor node designed for low-power and low-cost video surveillance able to detect changes in the environment. The system is equipped with a CMOS video camera and a Pyroelectric InfraRed (PIR) sensor exploited to reduce remarkably the power consumption of the system in absence of events. The on-board microprocessor implements an NCC-based change detection algorithm. We analyze different configurations and characterize the system in terms of runtime execution and power consumption.
Enabling Technologies on Hybrid Camera Networks for Behavioral Analysis of Unattended Indoor Environments and Their Surroundings
"... This paper presents a layered network architecture and the enabling technologies for accomplishing vision-based behavioral analysis of unattended environments. Specifically the vision network covers both the attended environment and its surroundings by means of hybrid cameras. The layer overlooking ..."
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This paper presents a layered network architecture and the enabling technologies for accomplishing vision-based behavioral analysis of unattended environments. Specifically the vision network covers both the attended environment and its surroundings by means of hybrid cameras. The layer overlooking at the surroundings is laid outdoor and tracks people, monitoring entrance/exit points. It recovers the geometry of the site under surveillance and communicates people positions to a higher level layer. The layer monitoring the unattended environment undertakes similar goals, with the addition of maintaining a global mosaic of the observed scene for further understanding. Moreover, it merges information coming from sensors beyond the vision to deepen the understanding or increase the reliability of the system. The behavioral analysis is demanded to a third layer that merges the information received from the two other layers and infers knowledge about what happened, happens and will be likely happening in the environment. The paper also describes a case study that was implemented in the Engineering Campus of the University of Modena and Reggio Emilia, where our surveillance system has been deployed in a computer laboratory which was often unaccessible due to lack of attendance.
