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STDnet-ST: Spatio-temporal ConvNet for small object detection
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DOI：10.1016/j.patcog.2021.107929.png)
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Abstract
En 中文
Object detection through convolutional neural networks is reaching unprecedented levels of precision. However, a detailed analysis of the results shows that the accuracy in the detection of small objects is still far from being satisfactory. A recent trend that will likely improve the overall object detection suc-cess is to use the spatial information operating alongside temporal video information. This paper intro-duces STDnet-ST, an end-to-end spatio-temporal convolutional neural network for small object detection in video. We define small as those objects under 16 x 16 px, where the features become less distinc-tive. STDnet-ST is an architecture that detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing those small objects. This permits to link the small objects across the time as tubelets. Furthermore, we propose a procedure to dismiss unprofitable object links in order to provide high quality tubelets, increasing the accuracy. STDnet-ST is evaluated on the publicly accessible USC-GRAD-STDdb, UAVDT and VisDrone2019-VID video datasets, where it achieves state-of-the-art results for small objects. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Small object detection
Spatio-temporal convolutional network
Object linking
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