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Detection and Tracking of Motorcycles in Congested Urban Environments Using Deep Learning and Markov Decision Processes

  • Jorge E. EspinosaEmail author
  • Sergio A. Velastin
  • John W. Branch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)

Abstract

This research describes “EspiNet”, a Deep Learning Convolutional Neural Network model, in conjunction with a Markov Decision Process (MDP) tracker for detection and tracking of occluded motorcycles in urban environments. The model is trained and evaluated, using a new public dataset with up to 10,000 annotated images, created for this research, and captured in real urban traffic scenes. Images were captured using a moving camera mounted in a drone, where more than 60% of the motorcycles are affected by occlusions. The network design involves many tests, where a promising result of 88.84% in average precision (AP) is achieved, despite the considerable number of occluded vehicles, the movement of the camera and the low angle used for capture. The model predictions are used as input to an MDP tracker, reaching results up to 85.2% in Multiple Object Tracking Accuracy (MOTA). The proposed network architecture outperforms state of the art YOLO (You Look Only Once) v3.0 and Faster R-CNN (VGG16 based) detection models, producing also better tracking results in comparison with the use of the other two models as detector base for the MDP tracker.

Keywords

Motorcycle detection Motorcycle tracking Faster R-CNN Region based detector CNN Deep learning Occluded images Markov Decision Process 

Notes

Acknowledgments

This work was partially supported by COLCIENCIAS project: Reduccion de Emisiones Vehiculares Mediante el Modelado y Gestion Optima de Trafico en Areas Metropolitanas - Caso Medellin - Area Metropolitana del Valle de Aburra, codigo 111874558167, CT 049-2017. Universidad Nacional de Colombia. Proyecto HERMES 25374. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Politécnico Colombiano Jaime Isaza CadavidMedellínColombia
  2. 2.Cortexica Vision Systems Ltd.LondonUK
  3. 3.Queen Mary University of LondonLondonUK
  4. 4.University Carlos III MadridMadridSpain
  5. 5.Universidad Nacional de Colombia – Sede Medellí­nMedellínColombia

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