To achieve significant improvements in public transport it is necessary to develop an autonomous system that locates and counts passengers in real time in scenarios with a high level of occlusion, providing tools to efficiently solve problems such as reduction and stabilization in travel times, greater fluency, better control of fleets and less congestion. A deep learning method based in transfer learning is used to accomplish this: You Only Look Once (YOLO) version 3 and Faster RCNN Inception version 2 architectures are fine tuned using PAMELA-UANDES dataset, which contains annotated images of the boarding and alighting of passengers on a subway platform from a superior perspective. The locations given by the detector are passed through a multiple object tracking system implemented based on a Markov decision process that associates subjects in consecutive frames and assigns identities considering overlaps between past detections and predicted positions using a Kalman filter.
|Number of pages||8|
|State||Published - 7 Oct 2021|
|Event||11th International Conference of Pattern Recognition Systems, ICPRS 2021 - Virtual, Online|
Duration: 17 Mar 2021 → 19 Mar 2021
|Conference||11th International Conference of Pattern Recognition Systems, ICPRS 2021|
|Period||17/03/21 → 19/03/21|
Bibliographical notePublisher Copyright:
© 2021 Institution of Engineering and Technology. All rights reserved.
- Deep learning
- Faster R-CNN
- Object detection
- Passenger counting
- YOLO v3