Exploiting Feature Fusion With Deep Learning-Based Next-Generation Consumer Products Detection on Video Surveillance Monitoring Systems

Video surveillance systems have been instrumental in the consumer electronics industry, providing advanced functionalities such as product detection. This system utilizes sophisticated techniques to analyze real-time video feeds, enabling us to detect and track different objects, like products. By incorporating product detection abilities into video surveillance technology, consumers may improve security measures while gaining meaningful information in personalized advertising, inventory management, and retail analytics. This convergence of product detection and video surveillance opens new possibilities for businesses to optimize customer experiences, operations, and security. By leveraging deep learning (DL) for product detection, companies can provide enhanced consumer experiences, improve efficiency, and streamline operations. The incorporation of DL into video surveillance systems represented a breakthrough in the intersection of technology and security within the consumer electronics field. This study develops a new feature fusion technique with deep learning-based next-generation consumer products detection (FFDL-NGCPD) on video surveillance monitoring systems. The main aim of the FFDL-NGCPD system is to detect and classify consumer products. In the FFDL-NGCPD technique, a feature fusion process comprises two DL models: ShuffleNet and MobileNet. To improve the performance of the FFDL-NGCPD technique, the spider monkey optimization (SMO) algorithm is applied for the hyperparameter selection process. Finally, an extreme learning machine (ELM) classifier recognizes the various consumer products. The performance evaluation of the FFDL-NGCPD approach is verified utilizing a benchmark dataset. An extensive comprehensive study underlined the enhanced detection results of the FFDL-NGCPD technique.