Human Activity Recognition with Privacy Preserving using Deep Learning Algorithms

Human activity recognition is an extensively researched topic in the field of computer vision. Recognizing human activities without revealing a person’s identity is one such use case. To solve this, we propose a practical method for human activity recognition (HAR) while maintaining anonymity. It captures and distributes data from a variety of sources while respecting the privacy of the individuals concerned. At the core of our approach is (DBN-RGMAA) based on deep neural networks, which are not only more accurate but can also be deployed in real-time video surveillance systems. Hence, this work presents a deep learning-based scheme for privacy-preserving human activities. Initially, for extracting the features from raw video data, a Deep Belief Network (DBN) is used. To increase the HAR identification rate, Hybrid Deep Fuzzy Hashing Algorithm (HDFHA) is employed to capture dependencies between two actions. Finally, the privacy model enhances the privacy of humans while permitting a highly accurate approach towards action recognition by the Recursive Genetic Micro-Aggregation Approach (RGMAA). The implementation is executed and the performances are evaluated by Accuracy, Precision, Recall, and F1 Score. A dataset named HMDB51 is used for empirical study. Our experiments using the Python data science platform reveal that the OPA-PPAR outperforms existing methods.