Research Article
Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles
Table 5
Comparisons of average running time per image and the GPU memory cost between two variants of our method and compared methods for extracting ConvNet features. The total running time consists of the computational costs for preprocessing, going through the Caffe and postprocessing. Note that /VGG-M1024b refer to the costs of computation and GPU memory when sending 100 detected landmarks into Caffe as a batch of 100. “—” means the computational cost is negligible. We can clearly see that the computation speed and GPU memory consumption of two variants of our method are close to those of FastRCNN-AlexNet/VGG-M1024 and several times faster and fewer than those of /VGG-M1024b.
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