Showing papers by "Malay K. Kundu published in 2019"

Turning video into traffic data – an application to urban intersection analysis using transfer learning

Abstract: With modern socio-economic development, the number of vehicles in metropolitan cities is growing rapidly. Therefore, obtaining real-time traffic volume estimates has a very important significance in using the limited road space and traffic infrastructure. In this study, the authors present a video-based traffic volume and direction estimation at road intersections. To discriminate the vehicles from the remaining foreground objects, vehicle recognition is performed by training a deep-learning architecture from a pre-trained model. This method, called transfer learning, primarily circumvents the requirement of huge labelled datasets and the time for training the network. The video sequence is first detected for moving foreground regions or patches. The trained model is subsequently used to classify the vehicles. The vehicles are tracked, and trajectory patterns are clustered using standard techniques. The number and direction of vehicles are noted, which are later compared with the manually observed values. All experiments were performed on real-life surveillance sequences recorded at four different traffic intersections in the city of Kolkata.

Interval type-2 fuzzy set and human vision based multi-scale geometric analysis for text-graphics segmentation

Abstract: This paper presents a novel method for texture-based text-graphic segmentation in a text embedded image. In the method, features are computed applying Multi-scale Geometric Analysis(MGA). The MGA of the image is done by Nonsubsampled contourlet transform(NSCT). The NSCT sub-bands help to generate the features which represent textures of the text portions and graphics portions of the image. In a segmentation process, the uncertainties arise mainly for two reasons: one is the ambiguity in gray level and other is the spatial ambiguity. Here the uncertainties are managed by interval type2 fuzzy set (IT2FS). The human vision model called human psychovisual phenomenon (HVS) is incorporated in the process for generating the interval type-2 fuzzy membership functions (IT2FMF). The efficiency of the proposed scheme is measured on the benchmark dataset. The robustness and performance bound of the proposed technique under noise corruption are measured statistically using modified Cramer-Rao bound. We found that effectiveness of the features by NSCT in combination with the IT2FS are quite promising in comparison to the state-of-the-arts methods.