Indoor Intrusion Detection and Filtering System Using Raspberry Pi

Razimi, Umi Najiah Ahmad; Alkawaz, Mohammed Hazim; Segar, Shamla Devi

doi:10.1109/cspa48992.2020.9068720

Cited by 5 publications

(1 citation statement)

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“…Tracking human movements is a challenging task applicable to a variety of fields, such as real-time exercise tracking [ 1 , 2 , 3 , 4 , 5 ], fall detection for the elderly [ 6 , 7 , 8 , 9 , 10 , 11 ], intrusion detection-based surveillance [ 12 , 13 ] in restricted areas such as railroad tracks and railway stations [ 14 ], etc. Wearable sensors [ 15 , 16 , 17 , 18 ] are popular for monitoring the health status in certain patients; however, these sensors only track certain vital signs and are insufficient for monitoring movement.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Human Motion Tracking by Tello EDU Drone

Boonsongsrikul

Eamsaard

2023

Sensors

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Human movement tracking is useful in a variety of areas, such as search-and-rescue activities. CCTV and IP cameras are popular as front-end sensors for tracking human motion; however, they are stationary and have limited applicability in hard-to-reach places, such as those where disasters have occurred. Using a drone to discover a person is challenging and requires an innovative approach. In this paper, we aim to present the design and implementation of a human motion tracking method using a Tello EDU drone. The design methodology is carried out in four steps: (1) control panel design; (2) human motion tracking algorithm; (3) notification systems; and (4) communication and distance extension. Intensive experimental results show that the drone implemented by the proposed algorithm performs well in tracking a human at a distance of 2–10 m moving at a speed of 2 m/s. In an experimental field of the size 95×35m2, the drone tracked human motion throughout a whole day, with the best tracking results observed in the morning. The drone was controlled from a laptop using a Wi-Fi router with a maximum horizontal tracking distance of 84.30 m and maximum vertical distance of 13.40 m. The experiment showed an accuracy rate for human movement detection between 96.67 and 100%.

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