Date of Publication :5th April 2025

Abstract: Once a futuristic concept confined to science fiction, traffic surveillance systems are now indispensable to modern urban planning and traffic management strategies. These systems, leveraging a complex interplay of sensors, communication networks, and sophisticated software, provide real-time data and analytical insights that enable authorities to monitor, understand, and optimize traffic flow. Traditional methods for traffic surveillance often struggle with accurately identifying traffic feature entities and objects. In most forums, Preliminary methods failed to identify the traffic feature entities' object presence based on traffic, leading to patterns of poor performance accuracy due to higher image degradation and false positives. So, the complexity increases to finding the traffic surveillance irregularities is difficult. To resolve this problem, we propose an advanced traffic surveillance system based on deep feature engineering with a Fuzzy Capsuled Convolution Neural Network (FC-CNN). The traffic videos are initially collected from a real-time traffic monitoring system to convert videos into frames. Then, preprocessing is carried out with an Adaptive Mean Wavelet Filter (AMWF) to normalize the image frames. Next, segmentation is carried out by Entity Pattern Watershed Segmentation (EPWS) to identify the object entities of traffic patterns objects entities based on feature recognitions. Initially, a fuzzy capsuled Convolution neural network is applied to find the irregularities of the helmet missing, triples flow, wrong route, and other anomalies. The proposed system produces a high traffic surveillance detection rate to perform best at a higher precision rate and improved detection accuracy in true favorable rates with redundant false rates. The proposed system archives improved accuracy compared to the other preliminary methods supporting traffic pattern rules. This results in superior performance compared to existing preliminary methods and supports the enforcement of traffic pattern rules with improved accuracy. This novel approach offers a robust and reliable solution for enhancing traffic surveillance and promoting safer road conditions.

Reference :