feature maps. There thus exist potential mismatches between the sizes of objects and filter receptive fields, as the object resolutions are variable, yet the sizes of filter receptive fields are fixed.

In practice, it sometimes leads to compromised results with particularly poor detections for far-scale objects.

Instead we consider a different strategy as follows. By engaging multiple convolutional feature layers, following the representation theory, outputs of higher layers encode semantic information of targets that are robust to significant appearance variations. However, their spatial resolutions tend to be too coarse to precisely localize the far-scale pedestrian instances. On the flip side, outputs of the lower convolutional layers provide more precise localization, but are less invariant to the objects appearance changes. This motivates us to consider learning a localization policy to actively identify the suitable representation layer as well as the appropriate filter size for the object of interest. Meanwhile, deep reinforcement learning techniques have been considered recently by [@CaiLaz:iccv15; @JieEtAl:NIPS16] to learn a localization policy that identifies the bounding-box of an object of interest, as well as by [@MnihEtAl:nips14] for digit recognition with a recurrent attention mechanism. Yet the impact of multiple pedestrians of different scales as in our scenarios are 