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Detecting foreground objects in video is crucial in various machine vision applications and computerized video surveillance technologies. Object tracking and detection are essential in object identification, surveillance, and navigation approaches. Object detection is the technique of differentiating between background and foreground features in a photograph. Recent improvements in vision systems, including distributed smart cameras, have inspired researchers to develop enhanced machine vision applications for embedded systems. The efficiency of featured object detection algorithms declines as dynamic video data increases as contrasted to conventional object detection methods. Moving subjects that are blurred, fast-moving objects, backdrop occlusion, or dynamic background shifts within the foreground area of a video frame can all cause problems. These challenges result in insufficient prominence detection. This work develops a deep-learning model to overcome this issue. For object detection, a novel method utilizing YOLOv3 and MobileNet was built. First, rather than picking predefined feature maps in the conventional YOLOv3 architecture, the technique for determining feature maps in the MobileNet is optimized based on examining the receptive fields. This work focuses on three primary processes: object detection, recognition, and classification, to classify moving objects before shared features. Compared to existing algorithms, experimental findings on public datasets and our dataset reveal that the suggested approach achieves 99% correct classification accuracy for urban settings with moving objects. Experiments reveal that the suggested model beats existing cutting-edge models by speed and computation.

Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).