Human detection and tracking with knee-high mobile 2D LIDAR

Taipalus, Tapio; Ahtiainen, Juhana

doi:10.1109/robio.2011.6181529

Cited by 29 publications

(10 citation statements)

References 17 publications

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“…However, all of the vision-based sensors suffer limitations in different lighting and weather conditions. In industrial safety applications, the standardized sensor for safe human detection is LIDAR (Light Detection and Ranging), where reflections from human legs at knee level are processed to identify presence of a human in the scanned area [4]. LIDAR sensors, though have many limitations in detecting reflections from dark surfaces and problems with outdoor harsh environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Micro-doppler based human-robot classification using ensemble and deep learning approaches

Abdulatif

Qian

Aziz

et al. 2018

2018 IEEE Radar Conference (RadarConf18)

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Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler (µ-D) and micro-Range (µ-R), respectively. Different moving targets will have unique µ-D and µ-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99% accuracy in identifying humans from robots on a single R-D map.

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Section: Introductionmentioning

confidence: 99%

Micro-doppler based human-robot classification using ensemble and deep learning approaches

Abdulatif

Qian

Aziz

et al. 2018

2018 IEEE Radar Conference (RadarConf18)

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“…Two-dimensional LiDARs project a single beam on a rotating mirror, while 3D LiDARs use multiple laser diodes that rotate at a very high speed; the higher the number of laser diodes, the more measurements can be acquired and the more accurate the perception task becomes [24]. Multiple 2D LiDARs (single beam) have been used in vehicle detection [25] and pedestrian detection [26,27] by applying pattern recognition techniques; however, this limits the detection to limited object classes.…”

Section: Point Clouds Acquired By Lidarsmentioning

confidence: 99%

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Khatab

Onsy

Abouelfarag

2022

Sensors

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One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios.

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“…2) 2D LIDARs: In research concerning transportation and mobile robotics, it is also common to apply a 2D LIDAR sensor for different purposes, like Simultaneous Localization and Mapping (SLAM) [28], detection and tracking [29]. 2D laser scanners are rarely applied standalone for these tasks, naturally 2.5D or 3D reconstruction is not even possible relying only on 2D data.…”

Section: A Sensors and Data Structurementioning

confidence: 99%

“…Although there are some successful attempts: for example in [30], where pedestrian detection is implemented based on spatial-temporal walking patterns. Another example is [29], where humans were detected and tracked in mobile 2D LIDAR data. This was done by euclidean clustering of data points, cluster matching and making a heuristic based decision.…”

Section: A Sensors and Data Structurementioning

confidence: 99%

Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor

Rózsa

Szirányi

2018

IEEE Trans. Intell. Transport. Syst.

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Environment analysis of automatic vehicles needs the detection from 3-D point cloud information. This paper addresses this task when only partial scanning data are available. Our method develops the detection capabilities of autonomous vehicles equipped with 3-D range sensors for navigation purposes. In industrial practice, the safety scanners of automated guided vehicles (AGVs) and a localization technology provide an additional possibility to gain 3-D point clouds from planar contour points or low vertical resolution. Based on this data and a suitable evaluation algorithm, intelligence of vehicles can be significantly increased without the need for installation of additional sensors. In this paper, we propose a solution for an obstacle categorization problem for partial point clouds without shape modeling. The approach is tested for a known database, as well as for real-life scenarios. In case of AGVs, real-time run is provided by on-board computers of usual complexity.

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Human detection and tracking with knee-high mobile 2D LIDAR

Cited by 29 publications

References 17 publications

Micro-doppler based human-robot classification using ensemble and deep learning approaches

Micro-doppler based human-robot classification using ensemble and deep learning approaches

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor

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