Pankaj Pratap Singh

Work place: Department of Computer Science & Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, India



Research Interests: Data Processing, Data Mining, Image Processing


Pankaj Pratap Singh received B.E. in Computer Science & Engineering Department from Dr. B. R. Ambedkar University, Agra, India in 2003, M.Tech (IT) from Indian Institute of Information Technology, Allahabad, India in 2008 and Ph.D. (2015) in Geomatics Engg., CED from Indian Institute of Technology Roorkee, India. Presently, Dr. Singh is an Assistant Professor in Computer Science & Engineering Department, Central Institute of Technology (CIT) Kokrajhar. He has published more than 45 research papers in International and National Journals/conferences. He is a senior member of IEEE, and also members of IUPRAI, SCRS, SPIE. His areas of interest are Satellite Image Processing, Computer Vision, Digital Image Processing, and Data Mining.

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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