UAVid dataset is a high-resolution UAV semantic segmentation dataset focusing on street scenes. The dataset consists of 4K high-resolution video sequences captured in oblique views. The dataset brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation.
The task for UAVid dataset is to predict per-pixel semantic labelling for the UAV video sequences. The original video file for each sequence is provided together with the labelled images. Currently, UAVid only supports image level semantic labelling without instance level consideration.
The semantic labelling performance is assessed based on the standard Jaccard Index, more known as the PASCAL VOC intersection-over-union metric.
IoU = TP /(TP+FP+FN).
TP, FP and FN are the numbers of true positive, false positive and false negative respectively, which can be calculated through the confusion matrix determined over all data from test split.
The goal for this task is to achieve as high IoU score as possible. For UAVid dataset, clutter class has a relatively large pixel number ratio and consists of meaningful objects, which is taken as one class for both training and evaluation rather than being ignored.
Researcher hereby agrees to the following terms and conditions:
Start: Jan. 1, 2020, midnight
Description: 30 sequence version. Submit your model prediction results and the performance will be evaluated.
Start: May 1, 2020, midnight
Description: 42 sequence version. Submit your model prediction results and the performance will be evaluated.
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