Real time fuzzy based traffic flow estimation and analysis

Real-time traffic flow analysis using road mounted surveillance cameras presents multitude of benefits. In this paper, we used surveillance videos to design optical flow based technique for robust motion analysis and estimation. Region growing method is employed for detection of objects of interest. Autonomous density estimation of vehicles is crucial for traffic congestion analysis so that countermeasures can be taken at the earliest possible opportunity. A video based data extraction scheme for traffic data is proposed to determine the right traffic conditions which alleviates the false alarms and detrimental noise effects. Evaluation of proposed system is done by applying approach on several surveillance videos obtained from different sources and scenarios. An experimental study illustrates estimation and analysis results accuracy as compared to state-of-the-art approaches

Tempo-spatial compactness based background subtraction for vehicle detection and tracking

Background modelling techniques use the time, spatial, intensity and image plane information to detect the objects. These features are integrated to extract the maximum information. The utilization of background techniques are mostly dependent on various parameters that can be learning rate or threshold. High dependency on parameters increase the complexity and make it difficult to control in changing weather conditions. Parameters based techniques do not provide the high efficiency in outdoor computer vision applications where illumination conditions are difficult to predict. This paper presents an algorithm that is based on background modelling with less dependency on parameters and robust to illumination changes. Camera jitter causes the major effect in modelling techniques so camera jitter is also addressed. A new way of separation of shadow from object is also implemented. Performance of the algorithm is compared with other state-of-the-art methods