640$\times$480 30Hz video footage taken from a vehicle driving through regular traffic in urban areas, which finally amounts to about 250,000 frames over around 2,300 unique pedestrians. The ETH benchmark dataset contains 3 testing video sequences with a resolution of 640$\times$480, and a frame rate of 13FPS. Finally, the TUD-Brussels pedestrian dataset is recorded from a driving car with a resolution of 640$\times$480 in the inner city of Brussels. For all the comparison methods considered, their own publicly available results are directly put in use throughout our experiments.

Ablation Experiments
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We evaluate the effectiveness of the two major modules in our approach, namely, the convolutional network for initial proposals and multi-layer representations and the active pedestrian detector. Without loss of generality, ablation experiments are carried out on the widely used Caltech benchmark. Three ablation scenarios are examined: First, we look into the effect of removing the second module. In other words, our system at this moment is reduced to directly use the initial proposals from multi-layer representations as the final output, which is accomplished without the follow-up active detection module; Second, we consider the scenario of having only our second module up and running, i.e. without the initial proposals 