Small-Scale and Occluded Pedestrian Detection Using Multi Mapping Feature Extraction Function and Modified Soft-NMS

In autonomous driving and Intelligent transportation systems, pedestrian detection is vital in reducing traffic accidents. However, detecting small-scale and occluded pedestrians is challenging due to the ineffective utilization of the low-feature content of small-scale objects. The main reasons behind this are the stochastic nature of weight initialization and the greedy nature of nonmaximum suppression. To overcome the aforesaid issues, this work proposes a multifocus feature extractor module by fusing feature maps extracted from the Gaussian and Xavier mapping function to enhance the effective receptive field. We also employ a focused attention feature selection on a higher layer feature map of the single shot detector (SSD) region proposal module to blend with its low-layer feature to tackle the vanishing of the feature detail due to convolution and pooling operation. In addition, this work proposes a decaying nonmaximum suppression function considering score and Intersection Over Union (IOU) parameters to tackle high miss rates caused by greedy nonmaximum suppression used by SSD. Extensive experiments have been conducted on the Caltech pedestrian dataset with the original annotations and the improved annotations. Experimental results demonstrate the effectiveness of the proposed method, particularly for small and occluded pedestrians.