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Remote Sensing
Volume 13
Issue 9
10.3390/rs13091670
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Article

MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images

by
Danilo Avola
1,*,
Luigi Cinque
1,
Anxhelo Diko
1,
Alessio Fagioli
1,
Gian Luca Foresti
2,
Alessio Mecca
1,
Daniele Pannone
1 and
Claudio Piciarelli
2
1
Department of Computer Science, Sapienza University, 00198 Rome, Italy
2
Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(9), 1670; https://doi.org/10.3390/rs13091670
Submission received: 17 March 2021 / Revised: 21 April 2021 / Accepted: 22 April 2021 / Published: 25 April 2021
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
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Abstract

Tracking objects across multiple video frames is a challenging task due to several difficult issues such as occlusions, background clutter, lighting as well as object and camera view-point variations, which directly affect the object detection. These aspects are even more emphasized when analyzing unmanned aerial vehicles (UAV) based images, where the vehicle movement can also impact the image quality. A common strategy employed to address these issues is to analyze the input images at different scales to obtain as much information as possible to correctly detect and track the objects across video sequences. Following this rationale, in this paper, we introduce a simple yet effective novel multi-stream (MS) architecture, where different kernel sizes are applied to each stream to simulate a multi-scale image analysis. The proposed architecture is then used as backbone for the well-known Faster-R-CNN pipeline, defining a MS-Faster R-CNN object detector that consistently detects objects in video sequences. Subsequently, this detector is jointly used with the Simple Online and Real-time Tracking with a Deep Association Metric (Deep SORT) algorithm to achieve real-time tracking capabilities on UAV images. To assess the presented architecture, extensive experiments were performed on the UMCD, UAVDT, UAV20L, and UAV123 datasets. The presented pipeline achieved state-of-the-art performance, confirming that the proposed multi-stream method can correctly emulate the robust multi-scale image analysis paradigm.
Keywords: UAV; object detection; tracking; deep learning; aerial images UAV; object detection; tracking; deep learning; aerial images
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MDPI and ACS Style

Avola, D.; Cinque, L.; Diko, A.; Fagioli, A.; Foresti, G.L.; Mecca, A.; Pannone, D.; Piciarelli, C. MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images. Remote Sens. 2021, 13, 1670. https://doi.org/10.3390/rs13091670

AMA Style

Avola D, Cinque L, Diko A, Fagioli A, Foresti GL, Mecca A, Pannone D, Piciarelli C. MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images. Remote Sensing. 2021; 13(9):1670. https://doi.org/10.3390/rs13091670

Chicago/Turabian Style

Avola, Danilo, Luigi Cinque, Anxhelo Diko, Alessio Fagioli, Gian Luca Foresti, Alessio Mecca, Daniele Pannone, and Claudio Piciarelli. 2021. "MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images" Remote Sensing 13, no. 9: 1670. https://doi.org/10.3390/rs13091670

APA Style

Avola, D., Cinque, L., Diko, A., Fagioli, A., Foresti, G. L., Mecca, A., Pannone, D., & Piciarelli, C. (2021). MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images. Remote Sensing, 13(9), 1670. https://doi.org/10.3390/rs13091670

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