Efficient indexing for object recognition using large networks

Template matching is an effective means of locating vehicles in outdoor scenes, but it tends to be a computationally expensive. To reduce processing time, we use large neural networks to predict, or index, a small subset of templates that are likely to match each window in an image. Results on actual LADAR range images show that limiting the templates to those selected by the neural networks reduces the computation time by a factor of 5 without sacrificing the accuracy of the results.