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.

本研究提出了一种利用级联阶段设计的自回归行人检测框架，其中采用了轻量级堆叠式解码器-编码器模块，使用卷积重采样层来提高特征，并保持高效的内存和运行时成本。此框架在区域提议网络中设计，相较于独立处理的 RoI 系统，保留了更大的附近检测范围内的上下文信息。在每个连续阶段分配严格的标签策略以鼓励不断提高精度。本研究提出的自回归框架在 Caltech 行人数据集中的合理和遮挡设置以及 KITTI 数据集上均实现了新的最佳表现。

带自回归网络阶段的行人检测