Pedestrian Detection with Autoregressive Network Phases

Abstract

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.

References in corpus (18)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Residual Learning for Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• WaveNet: A Generative Model for Raw Audio
• DSSD : Deconvolutional Single Shot Detector
• Going Deeper with Convolutions
• Stacked Hourglass Networks for Human Pose Estimation
• Feature Pyramid Networks for Object Detection
• Cascade R-CNN: Delving into High Quality Object Detection
• Path Aggregation Network for Instance Segmentation
• Learned in Translation: Contextualized Word Vectors
• Scale-aware Fast R-CNN for Pedestrian Detection
• How Far are We from Solving Pedestrian Detection?
• Illuminating Pedestrians via Simultaneous Detection & Segmentation
• Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
• Accurate Single Stage Detector Using Recurrent Rolling Convolution
• Repulsion Loss: Detecting Pedestrians in a Crowd
• Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection