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Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN) -like detection structure is employed. In this setting,(More)
Aerial traffic surveillance requires algorithms that can accurately predict the locations and orientations of hundreds of vehicles in a large high resolution aerial image within seconds. Under this constraint, the classical cascaded detection framework based on boosting algorithms still remains an optimal choice. These methods, however, usually use many(More)
High resolution aerial image based vehicle localization and categorization methods are crucial for many real life applications. Convolutional neural network based classifiers have already achieved very high performances, but are still suffering from the problem of class imbalance. To address this issue, an efficient bi-partite style network extension scheme(More)
Current researches on moving object detection from airborne videos are mainly based on frame difference. Though many improvements have been made on these methods, it is still difficult to extract all the moving pixels accurately. Being capable of providing more reliable motion information, background subtraction based methods have been widely used for(More)
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