Shitala Prasad

Work place: Institute for Infocomm Research, A*STAR, Singapore, Singapore



Research Interests: Deep Learning, Computer Vision, Graph and Image Processing


Shitala Prasad is an Assistant Professor at the School of Mathematics and Computer Science, IIT Goa, India. With a Ph.D. from IIT Roorkee, he specializes in deep learning, pattern recognition, and computer vision, particularly in image analysis and understanding. His expertise extends to various applications, including biometrics, biomedical, agriculture/agroindustry, and industrial domains. Before joining IIT Goa, He worked as a Scientist at the Institute of Infocomm Research (I2R), A*STAR Singapore, and held a postdoctoral position at CNRS France and NTU Singapore, respectively. As a Senior IEEE Member, he is recognized for significant contributions to the scientific community, marked by expert talks and noteworthy publications.

Author Articles
A Hybrid Approach for Real-time Vehicle Monitoring System

By Pankaj Pratap Singh Shitala Prasad

DOI:, Pub. Date: 8 Feb. 2024

In today's modern era, with the significant increase in the number of vehicles on the roads, there is a pressing need for an advanced and efficient system to monitor them effectively. Such a system not only helps minimize the chances of any faults but also facilitates human intervention when required. Our proposed method focuses on detecting vehicles through background subtraction, which leverages the benefits of various techniques to create a comprehensive vehicle monitoring solution. In general, when it comes to surveillance and monitoring moving objects, the initial step involves detecting and tracking these objects. For vehicle segmentation, we employ background subtraction, a technique that distinguishes foreground objects from the background. To target the most prominent regions in video sequences, our method utilizes a combination of morphological techniques. The advancements in vision-related technologies have proven to be instrumental in object detection and image classification, making them valuable tools for monitoring moving vehicles. Methods based on moving object detection play a vital role in real-time extraction of vehicles from surveillance videos captured by street cameras. These methods also involve the removal of background information while filtering out noisy data. In our study, we employ background subtraction-based techniques that continuously update the background image to ensure efficient output. By adopting this approach, we enhance the overall performance of vehicle detection and monitoring.

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