Computer vision based traffic monitoring system for multi-track freeways

Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic

Image matching using moment invariants

Matching images using Mean Squared Error (MSE) and Peak Signal to Noise (PSNR) ratios does not well conform to the Human Visual System (HVS). When matching two images, HVS operates both globally and locally when it identifies features of a scenery and this process is not matched adequately by PSNR or MSE. A low MSE or very high PSNR may not necessarily mean that images are similar. Similarly, when images are similar as HVS would identify, the corresponding MSE may not be very low and PSNR may not be very high. However, quite recently, a new measure has been proposed to circumvent the drawbacks of PSNR or MSE. This measure known as Structural Similarity Measure (SSIM) has received acclaim due to its ability to produce results on a par with Human Visual System. However, experimental results indicate that noise and blur seriously degrade the performance of the SSIM metric. Furthermore, despite SSIM׳s popularity, it does not provide adequate insight into how it handles \u27structural similarity\u27 of images. We propose a new structural similarity measure based on approximation level of a given discrete Wavelet decomposition that evaluates moment invariants to capture the structural similarity with superior results over SSIM