A Novel Approach for Model-Based Pedestrian Tracking Using Automotive Radar

Held, Patrick; Steinhauser, Dagmar; Koch, Andreas; Brandmeier, Thomas; Schwarz, Uli

doi:10.1109/tits.2021.3066680

Cited by 23 publications

(6 citation statements)

References 44 publications

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“…Ref. [ 78 ] proposes a Novel Approach to Model-Based Pedestrian Tracking Using Automotive Radar (NMPT radar), utilizing radar data for model-based pedestrian tracking. Ref.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

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“…Ref. [ 78 ] proposes a Novel Approach to Model-Based Pedestrian Tracking Using Automotive Radar (NMPT radar), utilizing radar data for model-based pedestrian tracking. Ref.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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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%

“…Similarly, Rohling et al (2010) distinguish pedestrians and vehicles. Held et al (2022) use a kinematics‐informed motion model to detect leg and upper body motion based on micro‐Doppler signatures. They track legs using an Extended Kalman Filter (EKF).…”

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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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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“…The critical problem is to extract the dynamic part of the environment from sensory data, similar to tracking objects in an unknown environment. In computer vision, object identification methods are classified into four categories; motion-based, model-based, appearance-based, and feature-based [ 29 – 32 ]. However, in the case of laser data, the appearance-based method may not be applied directly due to limited information.…”

Section: Introductionmentioning

confidence: 99%

A featureless approach for object detection and tracking in dynamic environments

et al. 2023

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One of the challenging problems in mobile robotics is mapping a dynamic environment for navigating robots. In order to disambiguate multiple moving obstacles, state-of-art techniques often solve some form of dynamic SLAM (Simultaneous Localization and Mapping) problem. Unfortunately, their higher computational complexity press the need for simpler and more efficient approaches suitable for real-time embedded systems. In this paper, we present a ROS-based efficient algorithm for constructing dynamic maps, which exploits the spatial-temporal locality for detecting and tracking moving objects without relying on prior knowledge of their geometrical features. A two-prong contribution of this work is as follows: first, an efficient scheme for decoding sensory data into an estimated time-varying object boundary that ultimately decides its orientation and trajectory based on the iteratively updated robot Field of View (FoV); second, lower time-complexity of updating the dynamic environment through manipulating spatial-temporal locality available in the object motion profile. Unlike existing approaches, the snapshots of the environment remain constant in the number of moving objects. We validate the efficacy of our algorithm on both V-Rep simulations and real-life experiments with a wide array of dynamic environments. We show that the algorithm accurately detects and tracks objects with a high probability as long as sensor noise is low and the speed of moving objects remains within acceptable limits.

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A Novel Approach for Model-Based Pedestrian Tracking Using Automotive Radar

Cited by 23 publications

References 44 publications

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

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

A featureless approach for object detection and tracking in dynamic environments

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