Speed Estimation and Detection of Moving Vehicles Based on Probabilistic Principal Component Analysis and New Digital Image Processing Approach

TLDR: In this chapter, the Probabilistic Principal Component Analysis (PPCA) method is proposed to detect multiple outliers in objects which is computationally fast and robust in identifying outliers which helps to reduce the dimension of video by finding an alternate set of coordinates.

Abstract: In the twenty-first century, smart city surveillance management is one of the advancements of Information and Communication Technology. Intelligent Transport System (ITS) is an essential component of the smart city. Moving vehicle detection and speed estimation are major tasks of traffic management. Vehicle tracking and speed measurement methods failed to achieve good accuracy rate due to unsuccessful detection of moving vehicles. In the existing system, the conventional de-noising filters reduce the noise in smooth regions. The edges of object boundaries are not sharply identified. In this chapter, the Probabilistic Principal Component Analysis (PPCA) method is proposed to detect multiple outliers in objects. It is computationally fast and robust in identifying outliers which helps to reduce the dimension of video by finding an alternate set of coordinates. The proposed approach consists of three stages. First, Spatio-temporal Varying Filter (STVF) is applied to preprocess extracted frames. Contour finding algorithm is used to detect the vehicle. The frame count scheme is applied to estimate the vehicle speed. This approach provides high detection accuracy with high precision and recall rate in BrnoCompSpeed dataset.