Multiple Pedestrian Detection Depending on Faster Region-based Convolutional Neural Network (RCNN)

Abstract

Pedestrian detect plays a crucial role in security, intelligent surveillance, vehicles, and robotics. Occlusion handling is a challenging worry in tracking multiple people. The tracking is based on the highest accuracy object detectors. In the current paper, we proposed a framework that detects multiple pedestrians in the image, which depends on Faster Region-based Convolutional Neural Network (R-CNN). We applied the transfer learning concept by using the VGG19 & VGG16 deep networks, which are trained before on Image-Net to extract the feature map. Relying on trained weights, to reduce the time of training, we used the transfer learning concept. The framework was tested on Penn-Fudan pedestrian database. The pedestrian detection accuracy was measured by using the area under the curve (AUC) of the receiver operating characteristic (ROC) that e is achieved 95.6%. In addition, the proposed system achieved Miss Rate (MR) equals 1.98, accuracy (ACC) equals 97.31%, and F1-score equals 93.17%. The achieved results show the promise of our proposed technique to detect multiple pedestrians in a single scene.