Privacy preserving human activity recognition framework using an optimized prediction algorithm
چکیده
Human activity recognition, in computer vision research, is the area of growing interest as it has plethora real-world applications. Inferring actions from one or more persons captured through a live video its immense utility contemporary era. Same time, protecting privacy humans to be given paramount importance. Many researchers contributed towards this end leading preserving action recognition systems. However, having an optimized model that can withstand any adversary models strives disclose information. To address problem, we proposed algorithm known prediction for (OPA-PPAR) based on deep neural networks. It anonymizes content have adaptive defeats attacks adversaries. The enhances while permitting highly accurate approach recognition. implemented realize human framework (PPHARF). visual made using underlying adversarial learning process where anonymization model. A dataset named metabolome database (HMDB51) used empirical study. Our experiments with Python data science platform reveal OPA-PPAR outperforms existing methods.