Pedestrian Intention Detection Using Faster RCNN and SSD

Abstract

In the domain of Intelligent Monitoring, Smart Driving and Robotics, Pedestrian intention detection is a prime discipline of object recognition. Currently, several pedestrian detection techniques are proposed however, just a handful are re-ported in the domain of pedestrian ‘intention’ detection. Due to the complications of the image background and pedestrian posture diversity, pedestrian intention detection is still a challenge which requires concise algorithms. In this paper, Single Shot Detector (SSD) is compared with Faster Region Convolutional Neural Network (Faster RCNN) architecture of deep neural network by applying different Convolutional Neural Network (CNN) models. Experiments have been conducted in a wide spectrum to obtain various models of Faster RCNN and SSD through compatible alterations in algorithm and parameters tuning. In this paper, Faster R-CNN and SSD architecture have been trained and their results are compared. New and simple evaluation performance parameters are suggested namely: Percentage Detection Index, Percentage Recognition Index and Precision score as compared to the traditional mean average precision (mAp) found in literature. While training these architectures with 1350 images, Faster RCNN learned three times faster than SSD with 2% increased accuracy.