Recently, pedestrian detection system has become an important issue to improve the road safety through its integration in several Advanced Driver Assistance Systems (ADAS). According to the literature, the use of the Histogram of Oriented Gradients (HOG) descriptor has been very efficient and robust in the development of such systems. However, the intensive calculation steps of this descriptor make its implementation an issue to be address in order to meet real-time requirement. In this paper, an efficient hardware architecture based on an improved HOG version and linear SVM (Support Vector Machine) classifier is emphasized. The proposed architecture has founded on cell-based scanning, some approximations and without any pixelbuffers. The system evaluation was carried out on a Xilinx Zynq platform. Experimental results showed that our approach can achieve an efficient pedestrian detection for full HD (High-Definition) video with a reduced hardware resource and power consumption.

Security concerns have become paramount, especially with the increasing reliance on facial recognition technology for identity verification. The use of masks, while essential for health protection, poses a significant challenge for these systems, hindering accurate identification. This issue is particularly critical for organizations that depend on facial recognition for security and authentication, especially within IoT systems that are vulnerable to malicious activities. This article presents two major contributions to address these challenges. First, it enhances the detection of mask‐wearing individuals using a computer vision machine‐learning model, specifically a convolutional neural network (CNN). Second, it introduces a novel authentication framework for verifying users during communication between nodes and access points. The authentication system comprises three key phases. Initially, an advanced fusion technique at the access point level employs a unique hybrid biometric pattern that combines password and image features to bolster security. Next, secure transmission of this pattern between the node and the access point is ensured through AES cryptography and blockchain technology. Finally, a novel matching method compares password and image features against database records for verification, conducted during the development phase at the node level. Experimental analysis reveals that this approach delivers outstanding results, achieving 99% accuracy, 0.99 recall, 100% precision, and an F1 score of 0.989.