Radar based detection and tracking of a walking human

Ahtiainen, Juhana; Terho, Sami; Koponen, Sampsa

doi:10.3182/20100906-3-it-2019.00076

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…UWB sensors have also been shown to be well applicable in firefighting (Tiemann et al, 2020). Bartsch et al (2012) emphasize that human gait modeling approaches, like in Ahtiainen et al (2010), Rohling et al (2010) or Held et al (2022), always require a sequence of scans rather than a single frame to be analyzed. Therefore, they heuristically identify people by applying five features based on geometry and Doppler velocity.…”

Section: Radar-based Leg Trackingmentioning

confidence: 99%

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Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

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Leg tracking is an established field in mobile robotics and machine vision in general.These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail.Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state-of-the-art performance, and that laser and fusion tracker outperform recent methods.

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Section: Radar-based Leg Trackingmentioning

confidence: 99%

“…Zhang et al (2007) and Ahtiainen et al (2010) both detect legs by identifying walking patterns in micro‐Doppler signatures. Similarly, Rohling et al (2010) distinguish pedestrians and vehicles.…”

Section: Related Workmentioning

confidence: 99%

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

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“…In order to specifically detect pedestrian walking, there are also different algorithms used, which may include extraction and matching. In [22], the authors developed a method using a combination of automotive and Doppler radars to detect the motion components of pedestrians by applying Gaussian distribution and a Kalman filter. By analyzing the Fourier spectrogram of Doppler frequency, they can detect the motion of humans in periodically.…”

Section: Radarmentioning

confidence: 99%

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sighencea

Stanciu

Căleanu

2021

Sensors

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Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

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