<![CDATA[Tracking individuals in real-world environments requires robust, non-intrusive methods that overcome the limitations of device-based systems. This study proposes a convolutional neural network (CNN)-driven person-tracking framework that identifies targeted individuals directly from camera feeds, eliminating the need for wearable or global positioning system (GPS) devices and addressing a major drawback of traditional tracking technologies. The system utilizes a TensorFlow-trained CNN model that can detect, recognize, and locate persons of interest in real-time, even under varying illumination conditions. Unlike conventional approaches, our method integrates facial feature extraction with encrypted identity management, enabling secure multi-person detection and rapid location reporting. Experimental results demonstrate a 92% accuracy in low-light settings and 100% accuracy under normal lighting, confirming the system’s effectiveness for security-oriented applications. The findings highlight the novelty of combining lightweight CNN architecture, real-time facial recognition, and hash-based identity protection within a unified tracking pipeline.]]>
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