FuzzyAct: A Fuzzy-based Framework for Temporal Activity Recognition in IoT Applications using RNN and 3D-DWT

Despite massive research in deep learning, the human activity recognition (HAR) domain still suffers from key challenges in terms of accurate classification and detection. The core idea behind recognizing activities accurately is to assist internet of things (IoT) enabled smart surveillance systems. Thereby, this work is based on the joint use of discrete wavelet transform (DWT) and recurrent neural network (RNN) to classify and detect human activities accurately. Recent approaches on HAR exploit 3D CNNs to extract spatial information, which adds a computational burden. The feature extraction process is an integral part of HAR; in our case, features are extracted using 3D-DWT instead of 3D CNNs, performed in three steps of 1D-DWT to reflect the Spatio-temporal features of human action. Given the features, the RNN produces an output label for each video clip taking care of the long-term temporal consistency among close predictions in the output sequence. It is noticed that feature extraction through 3D-DWT essentially recovers the multiple angles of an activity. Many HAR techniques distinguish an activity based on the posture of an image frame rather than learning the transitional relationship between postures in the temporal sequence, resulting in accuracy degradation. To address this problem, we designed a novel rank-based fuzzy approach that segregates activities precisely by ranking the probabilities of activities based on confidence scores. FuzzyAct achieved an average mAP of 0.8012 mAP on the ActivityNet dataset, and outperformed the baseline counterparts and other state-of-the- art approaches on benchmark datasets. Lastly, we present a mechanism to compress the proposed RNN for edge enabled internet of things (IoT) applications.