Shilpa Paygude

Work place: Maharashtra Institute of Technology, Pune, 411038, India



Research Interests: Image Processing, Embedded System, Computer Vision, Computer systems and computational processes


Shilpa Paygude is a PhD scholar of E&TC department in College of Engineering , Pune (COEP). She completed her Masters and Bachelors courses in E&TC Engineering from COEP in 2001 and 1990 respectively. She is currently working as an Associate Professor in Computer Engineering Department in Maharashtra Institute of Technology, Pune. Her areas of interest are Image Processing, Computer Vision, Microprocessors, Microcontrollers and Embedded System.

Author Articles
Velocity and Orientation Detection of Dynamic Textures Using Integrated Algorithm

By Shilpa Paygude Vibha Vyas Chinmay Khadilkar

DOI:, Pub. Date: 8 Dec. 2018

Dynamic Texture Analysis is a hotspot field in Computer Vision. Dynamic Textures are temporal extensions of static Textures. There are broadly two cat-egories of Dynamic Textures: natural and manmade. Smoke, fire, water and tree are natural while traffic and crowd are manmade Dynamic Textures. In this paper, an integrated efficient algorithm is discussed and proposed which is used for detecting two features of objects in Dynamic Textures namely, velocity and orientation. These two features can be used in identifying the velocity of vehicles in traffic, stampede prediction and cloud movement direction. Optical flow technique is used to obtain the velocity feature of the objects in motion.  Since optical flow is computationally complex, it is applied after background subtraction. This reduces the number of computations. Variance feature of Gabor filter is used to find the orientation which gives direction of movement of majority objects in a video. The combination of optical flow and Gabor filter technique together gives accurate orientation and velocity of Dynamic Texture with less number of computations in terms of time and algorithm.. Proposed algorithm can be used in real time applications. Velocity detection is done using Farneback Optical flow and orientation or angle detection is done using Bank of Gabor Filters The existing methods are used to calculate either velocity or orientation accurately individually. Varied datasets are used for experimentation and acquired results are validated for the selected database. 

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