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Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach

  • 1205: Emerging Technologies for Information Hiding and Forensics in Multimedia Systems
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Abstract

In the contemporary era, the global explosion of traffic has created many eye-catching concerns for policymakers. This not only enhances pollution but also leads to several road accident fatalities which may be greatly reduced by proper monitoring and surveillance. Further, with the advent of UAV technology and due to the incompatibility of traditional techniques, surveillance has become one of UAVs prominent application domains. However, it requires algorithmic analysis of aerial images which becomes extremely challenging due to multi-scale rotating objects with large aspect ratios, extremely imbalanced categories, cluttered background, and birds-eye view. Therefore, this article presents the novel aerial image traffic monitoring and surveillance algorithms based on the most advanced and popular DL object detection models (Faster-RCNN, SSD, YOLOv3, and YOLOv4) using the AU-AIR dataset. This dataset is exceedingly imbalanced and to resolve this issue, another 500 images have been grabbed by web-mining techniques. The novel contribution of this work is two-fold. First, this article scientifically distinguishes the inappropriateness of ground-view images for aerial object detection. Second, a regress comparison of these algorithms has been made to investigate their effectiveness. Extensive experimental analysis endorses the efficiency of YOLOv4 as it outperforms the other developed models by a minimum mAP margin of 88%. Also, more than 6 times high detection speed and greater adaptability with stronger detection robustness ensure its real-time practical implementation.

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Abbreviations

CNN:

Convolutional Neural Network

R-CNN:

Region-based Convolutional Neural Network

SPP:

Spatial Pyramid Pooling

DL:

Deep Learning

FPS:

Frames per second

SSD:

Single-shot detector

IoU:

Intersection over Union

SVM:

Support Vector Machine

mAP:

Mean Average Precision

UAV:

Unmanned aerial Vehicles

WHO:

World Health Organization

MAV:

Manned Aerial Vehicle

PANet:

Path Aggregation Network

YOLO:

You Only Look Once

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Acknowledgements

The first author would like to thank the Ministry of Human Resource Development, New Delhi, India for providing the Research Fellowship for carrying out this work. The authors would also like to thank ISRO, India for providing the support time to time to carry out this area of research.

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  1. Department of Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, India

    Himanshu Gupta & Om Prakash Verma

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Correspondence to Om Prakash Verma.

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Gupta, H., Verma, O.P. Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach. Multimed Tools Appl 81, 19683–19703 (2022). https://doi.org/10.1007/s11042-021-11146-x

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