Vehicle Density Prediction in Low Quality Videos with Transformer Timeseries Prediction Model (TTPM)

Abstract

Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India. The video obtained from such surveillance are of low quality. Still counting vehicles from such videos are necessity to avoid traffic congestion and allows drivers to plan their routes more precisely. On the other hand, detecting vehicles from such low quality videos are highly challenging with vision based methodologies. In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India, which is mostly an un-attempted entity by most available sources. In this work profound Detection Transformer (DETR) model is used for object (vehicle) detection. Here vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real attributes. Every frame in the traffic footage has its date and time which is detected and retrieved using Tesseract Optical Character Recognition. The date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR model. This furnishes the vehicles report with timestamp. Transformer Timeseries Prediction Model (TTPM) is proposed to predict the density of the vehicle for future prediction, here the regular NLP layers have been removed and the encoding temporal layer has been modified. The proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.