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

Every year, the \textit{Aedes aegypti} mosquito infects thousands of people with diseases such as dengue, zika, chikungunya, and urban yellow fever. The main form to combat these diseases is to avoid the transmitter reproduction by searching and eliminating the potential mosquito breeding grounds. In this work, we introduce a comprehensive database of aerial videos recorded with a drone, where all objects of interest are identified by their respective bounding boxes, and describe an object detection system based on deep neural networks. We track the objects by employing phase correlation to obtain the spatial alignment between them along the video frames. By doing so, we are capable of registering the detected objects, minimizing false positives and correcting most false negatives. Using the ResNet-101-FPN as a backbone, it is possible to obtain 0.78 in terms of \textit{F1-score} on the proposed dataset.