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Spatio-temporal Consistency to Detect Potential Aedes aegypti Breeding Grounds in Aerial Video Sequences

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Document pages: 6 pages

Abstract: Every year, the textit{Aedes aegypti} mosquito infects thousands of peoplewith diseases such as dengue, zika, chikungunya, and urban yellow fever. Themain form to combat these diseases is to avoid the transmitter reproduction bysearching and eliminating the potential mosquito breeding grounds. In thiswork, we introduce a comprehensive database of aerial videos recorded with adrone, where all objects of interest are identified by their respectivebounding boxes, and describe an object detection system based on deep neuralnetworks. We track the objects by employing phase correlation to obtain thespatial alignment between them along the video frames. By doing so, we arecapable of registering the detected objects, minimizing false positives andcorrecting most false negatives. Using the ResNet-101-FPN as a backbone, it ispossible to obtain 0.78 in terms of textit{F1-score} on the proposed dataset.

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