  We present an autoregressive pedestrian detection framework with cascaded
phases designed to progressively improve precision. The proposed framework
utilizes a novel lightweight stackable decoder-encoder module which uses
convolutional re-sampling layers to improve features while maintaining
efficient memory and runtime cost. Unlike previous cascaded detection systems,
our proposed framework is designed within a region proposal network and thus
retains greater context of nearby detections compared to independently
processed RoI systems. We explicitly encourage increasing levels of precision
by assigning strict labeling policies to each consecutive phase such that early
phases develop features primarily focused on achieving high recall and later on
accurate precision. In consequence, the final feature maps form more peaky
radial gradients emulating from the centroids of unique pedestrians. Using our
proposed autoregressive framework leads to new state-of-the-art performance on
the reasonable and occlusion settings of the Caltech pedestrian dataset, and
achieves competitive state-of-the-art performance on the KITTI dataset.
