Location-aware object detection via coherent region grouping

We present a scene adaptation algorithm for object detection. Our method discovers scene-dependent features discriminative to classifying foreground objects into different categories. Unlike previous works suffering from insufficient training data collected online, our approach incorporated with a similarity grouping procedure can automatically gather more consistent training examples from a neighbour area. Experimental results show that the proposed method outperforms several related works with higher detection accuracies.