Multi-sensor identity tracking with event graphs

Morton, P.; Douillard, Bertrand; Underwood, James

doi:10.1109/icra.2013.6631252

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…As outlined in [48], some of the drawbacks of CLEAR MOT metrics include inconsistent penalizing of tracking errors in some situations with groups of people, or its lack of penalizing ID switches in quick interactions from multiple people [50].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Person tracking and following with 2D laser scanners

Leigh

Pineau

Olmedo

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

158

127

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ACKNOWLEDGEMENTSThis thesis would not have been possible without the help of a great many people who provided me with inspiration, direction and encouragement.Foremost, I would like to thank my supervisor, Joelle Pineau. You've been the ideal mixture of supportive, easy-going and motivating during my master's. You were at once hands-off, letting me pursue the research ideas I was most interested in, and engaged, constantly providing me with valuable feedback on whatever we were working on. You listened to my ideas from day one so I would grow and learn to think for myself rather than follow orders. You were patient when research went slow and were quick to offer praise when things went well. I could not have asked for more from a supervisor. In this thesis we propose a new algorithm for robust detection, tracking and following from laser scanners mounted at leg height. We develop a set of quantitative benchmarks based on the CLEAR MOT metrics which demonstrate that the approach is more effective than currently available alternatives in a variety of environments, both indoor and outdoor, and on different robot platforms (the intelligent power wheelchair and a Clearpath Husky). To the best of our knowledge, these are the first benchmarks which evaluate person tracking performance from a mobile robot with laser scanners at leg height. We further integrate our novel person tracking system with a person following control system to demonstrate its usefulness in this application. The method has been implemented in the Robot Operating System (ROS) framework and will be publicly released as a ROS package. Dans cette thèse nous proposons un nouvel algorithme pour la détection, la localisation et le suivià partir de capteurs lasers situésà hauteur de la jambe.Nous développons un ensemble de mesures de référence quantitatives basées sur les métriques CLEAR MOT, qui démontrent que notre approche est plus efficace que les alternatives actuelles, ce dans une variété d'environnements,à la fois intérieurs et extérieurs, et sur différentes plateformes de robots (le fauteuil roulantélectrique intelligent et un Clearpath Husky). Au mieux de notre connaissance, nous sommes les premiersàévaluer la performance du suivi de personnes depuis un robot mobilé equipé de capteurs lasersà hauteur de la jambe. De plus, pour en démontrer l'utilité, nous intégrons notre nouveau système de localisation de personnes avec un système de contrôle automatique permettant au robot de se déplacer en suivant une personne.

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Section: Evaluation Metricsmentioning

confidence: 99%

Person tracking and following with 2D laser scanners

Leigh

Pineau

Olmedo

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

158

127

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“…In this work, we initialize the prior over associations in a more robust manner. Similar to the work by Morton et al (2013), we summarize the image information of individual objects before and after association ambiguities using a colour histogram as appearance model. We sequentially update the histogram of object i by averaging the one for the current assigned image patch (detection) and the current appearance model.…”

Section: Methodsmentioning

confidence: 99%

A variational approach to simultaneous multi-object tracking and classification

Romero-Cano

Agamennoni

Nieto

2015

The International Journal of Robotics Research

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Object tracking and classification serve as basic components for the different perception tasks of autonomous robots. They provide the robot with the capability of class-aware tracking and richer features for decision-making processes. The joint estimation of class assignments, dynamic states and data associations results in a computationally intractable problem. Therefore, the vast majority of the literature tackles tracking and classification independently. The work presented here proposes a probabilistic model and an inference procedure that render the problem tractable through a structured variational approximation. The framework presented is very generic, and can be used for various tracking applications. It can handle objects with different dynamics, such as cars and pedestrians and it can seamlessly integrate multi-modal features, for example object dynamics and appearance. The method is evaluated and compared with state-of-the-art techniques using the publicly available KITTI dataset.

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“…Since a new type of sensor has become popular due to the development of sensing devices, there have been many attempts to overcome the limitations of a particular type of the sensor by fusing various types of sensors such as lidar sensors and image sensors [13]- [17], [39]. However, the sensors mainly used in these studies are very expensive equipment, such as multilayer lidar, or are difficult to use in a small indoor environment, such as radar.…”

Section: Related Workmentioning

confidence: 99%

Multi-Target Tracking With Multiple 2D Range Scanners

et al. 2020

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We propose a novel efficient online method for tracking performers on stage. Most existing tracking methods have focused on using expensive, high-performing sensors, such as multilayer lidar sensors or high-resolution, short-range radars. Image sensor-based methods are not appropriate for tracking performers on stage because of challenging illumination conditions caused by lighting effects. In this paper, we introduce a robust multi-target tracking method based on the sensor fusing of two-dimensional distance sensors such as single-layer lidar sensors that have a relatively lower cost than the aforementioned types of sensors. In our method, measurements from each sensor are transformed into a reference coordinate system and objects are detected with those transformed measurements. Then, the object detections are used in generating or extending the trajectories of targets by detection-to-trajectory matching. In our experiments, we quantitatively evaluated the proposed method with a newly constructed dataset which consists of two scenarios simulating performances on stage. We collected the scan results of two single-layer lidar sensors and image frames captured by a camera sensor for each scenario. The experimental results show that the proposed method robustly tracks performers in challenging scenarios, in which the performers move abruptly and are densely located. INDEX TERMS Sensor fusion, multi-target tracking, moving object detection.

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Multi-sensor identity tracking with event graphs

Cited by 6 publications

References 11 publications

Person tracking and following with 2D laser scanners

Person tracking and following with 2D laser scanners

A variational approach to simultaneous multi-object tracking and classification

Multi-Target Tracking With Multiple 2D Range Scanners

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