Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach
Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/15/5856 |
| _version_ | 1797432637986439168 |
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| author | Alexandra Ștefania Ghiță Adina Magda Florea |
| author_facet | Alexandra Ștefania Ghiță Adina Magda Florea |
| author_sort | Alexandra Ștefania Ghiță |
| collection | DOAJ |
| description | Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving. |
| first_indexed | 2024-03-09T10:04:29Z |
| format | Article |
| id | doaj.art-7ca6100d68de4bcdadf6533c84fb2993 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:04:29Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7ca6100d68de4bcdadf6533c84fb29932023-12-01T23:10:44ZengMDPI AGSensors1424-82202022-08-012215585610.3390/s22155856Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based ApproachAlexandra Ștefania Ghiță0Adina Magda Florea1Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, RomaniaCurrently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.https://www.mdpi.com/1424-8220/22/15/5856person re-identification and trackingtrajectory predictioncomputer visionmachine learningsocial roboticspedestrian tracking |
| spellingShingle | Alexandra Ștefania Ghiță Adina Magda Florea Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach Sensors person re-identification and tracking trajectory prediction computer vision machine learning social robotics pedestrian tracking |
| title | Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach |
| title_full | Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach |
| title_fullStr | Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach |
| title_full_unstemmed | Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach |
| title_short | Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach |
| title_sort | real time people re identification and tracking for autonomous platforms using a trajectory prediction based approach |
| topic | person re-identification and tracking trajectory prediction computer vision machine learning social robotics pedestrian tracking |
| url | https://www.mdpi.com/1424-8220/22/15/5856 |
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