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Pedestrian detection for UAVs using cascade classifiers and saliency maps

Abstract:

In this paper, we proposed algorithm and dataset for pedestrian detection focused on applications with micro multi rotors UAV (Unmanned Aerial Vehicles). For training dataset we capture images from surveillance cameras at different angles and altitudes. We propose a method based on HAAR-LBP (Local Binary Patterns) cascade classifiers with Adaboost (Adaptive Boosting) training and, additionally we combine cascade classifiers with saliency maps for improving the performance of the pedestrian detector. We evaluate our dataset by the implementation of the HOG (Histogram of oriented gradients) algorithm with Adaboost training and, finally, algorithm performance is compared with other approaches from the state of art. The results shows that our dataset is better for pedestrian detection in UAVs, HAAR-LBP have better characteristics than HAAR like features and the use of saliency maps improves the performance of detectors due to the elimination of false positives in the image.