MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images
Autor: | Claudio Piciarelli, Alessio Fagioli, Anxhelo Diko, Luigi Cinque, Danilo Avola, Alessio Mecca, Gian Luca Foresti, Daniele Pannone |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Image quality
Computer science UAV Science 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 0202 electrical engineering electronic engineering information engineering Computer vision 021101 geological & geomatics engineering object detection tracking deep learning aerial images business.industry Detector Object (computer science) Pipeline (software) Object detection Kernel (image processing) Metric (mathematics) General Earth and Planetary Sciences Clutter 020201 artificial intelligence & image processing Artificial intelligence business |
Zdroj: | Remote Sensing, Vol 13, Iss 1670, p 1670 (2021) Remote Sensing; Volume 13; Issue 9; Pages: 1670 |
ISSN: | 2072-4292 |
Popis: | 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. |
Databáze: | OpenAIRE |
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