Performance Analysis of Inception-v2 and Yolov3-Based Human Activity Recognition in Videos

Abstract

Recently, many deep learning solutions have been proposed for human activity recognition (HAR) in videos. However, the HAR accuracies obtained using models is not adequate. In this work, we proposed two HAR architectures using Faster RCNN Inception-v2 and YOLOv3 as object detection models. In particular, we considered the human activities like walking, jogging and running as activities to be recognized by our proposed architectures. We used the pretrained Faster RCNN Inception-v2 and YOLOv3 object detection models. We then analyzed the performance of proposed architectures using benchmarked UCF-ARG dataset of videos. The experimental results show that Yolov3-based HAR architecture outperforms Inception-v2 in all scenarios.