ISGAN: An Improved SGAN for Crowd Trajectory Prediction from Surveillance Videos

Abstract

AI and computer vision are making strides every day with newer solutions to real-world problems and emerging challenges. An extremely important and useful application of computer vision is surveillance systems for security in open or closed spaces. This research work investigates a security application by forecasting crowd trajectories using surveillance videos. Generative Adversarial Networks(GANs) are commonly used for video processing applications like future frame synthesis. Social GAN(SGAN) is one of the recent models that has been used for crowd trajectory prediction. However, SGAN is a two stream architecture that keeps the background and foreground separate and predicts the foreground changes only, keeping the background static. In this paper, a novel GAN architecture called iSGAN is proposed for crowd trajectory prediction. The proposed model is an improvement of SGAN and it incorporates the onestream architecture of improved Video GAN (iVGAN) into SGAN. The one-stream architecture doesn't separate the backgroungd and foreground and thus allowing to captiure the dynamics of the video (camera zoom, pan, lighting change, obstacles etc.). The proposed iSGAN model has been assessed on the benchmark ETH Pedestrian dataset and it is found that the iSGAN Model outperforms other existing trajectory prediction models.