Despite being extensively used in numerous uses, precise and effective human activity identification continues to be an interesting research issue in the area of vision for computers. Currently, a lot of investigation is being done on themes like pedestrian activity recognition and ways to recognize people's movements employing depth data, 3D skeletal data, still picture data, or strategies that utilize spatiotemporal interest points. This study aims to investigate and evaluate DL approaches for detecting human activity in video. The focus has been on multiple structures for detecting human activities that use DL as their primary strategy. Based on the application, including identifying faces, emotion identification, action identification, and anomaly identification, the human occurrence forecasts are divided into four different subcategories. The literature has been carried several research based on these recognitions for predicting human behavior and activity for video surveillance applications. The state of the art of four different applications' DL techniques is contrasted. This paper also presents the application areas, scientific issues, and potential goals in the field of DL-based human behavior and activity recognition/detection.
Recently, Speech Emotion Recognition (SER) has become an important research topic of affective computing. It is a difficult problem, where some of the greatest challenges lie in the feature selection and representation tasks. A good feature representation should be able to reflect global trends as well as temporal structure of the signal, since emotions naturally evolve in time; it has become possible with the advent of Recurrent Neural Networks (RNN), which are actively used today for various sequence modeling tasks. This paper proposes a hybrid approach to feature representation, which combines traditionally engineered statistical features with Long Short-Term Memory (LSTM) sequence representation in order to take advantage of both short-term and long-term acoustic characteristics of the signal, therefore capturing not only the general trends but also temporal structure of the signal. The evaluation of the proposed method is done on three publicly available acted emotional speech corpora in three different languages, namely RUSLANA (Russian speech), BUEMODB (Turkish speech) and EMODB (German speech). Compared to the traditional approach, the results of our experiments show an absolute improvement of 2.3% and 2.8% for two out of three databases, and a comparative performance on the third. Therefore, provided enough training data, the proposed method proves effective in modelling emotional content of speech utterances.
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