Affiliations: [a] Department of Computer Science and Applications, Panjab University Swami Sarvanand Giri Regional Centre, Hoshiarpur, Punjab, India | [b] Delhi Skill and Entrepreneurship University, New Delhi, India | [c] Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India | [d] Center for Research and Innovation in Interdisciplinary Studies, Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi, India
Correspondence:
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Corresponding author: Mamta Mittal, Delhi Skill and Entrepreneurship University, New Delhi, India. E-mail: [email protected].
Abstract: The world’s population is growing at an exponential rate, which has created a slew of new issues in our daily lives. Among these challenges is the difficulty of finding available parking spaces. To overcome this obstacle, an effective system for detecting vacant parking spaces is crucial, particularly in densely populated areas. Even though there are numerous parking detecting systems on the market today, they all have their own set of restrictions. There are primarily two types of parking systems available. The first is a hardware-based system that uses hardware to detect whether a space is vacant or occupied, and the second is a smart parking system that uses cameras to classify parking status. Vision-based parking systems are becoming increasingly popular due to benefits such as flexibility and cost efficiency. However, due to challenging weather conditions and various occlusions caused by objects, such systems are challenging to develop. The limitations of a vision-based parking system must be addressed for it to become more accurate. To date, many researchers have attempted to incorporate deep learning-based parking systems with various algorithms but none of them has focused on reducing the complexity of Convolutional Neural Network (CNN) training through unsupervised pre-processing steps. In this study, an attempt is made to reduce the complexity of CNN by integrating salient features as a pre-processing step for normalizing weather conditions. It has been observed that salient features-based pre-processing makes the system more robust in terms of accuracy and time efficiency.
