# Human Activity Classification Using the 3 DCNN Architecture
## 1 Introduction  
The field of computer vision is mentioned often in the modern literature because of the success that neural networks are now achieving. In this paper, we address a neural network architecture called a 3D convolutional neural network (3 DCNN). This architecture is often used to detect complex images as well as videos, such as medical images. Here we use the 3 DCNN architecture for video classification. Video classification has been identified as a major challenge because of the type of information contained in video, namely, time continuity information. We not only need to look at the simple 2D space of an image but must also take into account the previous and following images to process information about time. When classifying a video, researchers have to deal with challenges such as the classification of abnormal behavior of people in public spaces, crowded scenes with a variety of abnormalities, and the classification of various human activities. Such research can be very helpful for applications such as improving safety or by protecting the elderly by identifying fall accidents and subsequently calling for help. Further, these techniques can be used to detect terrorists and to prevent a variety of dangerous events. The problem of classification is addressed in several papers, such as [1], where they aim to classify a person attacking an ATM from depth camera video. Weapons detection using neural networks for video processing has been studied, as in [2]. The recognition ofhuman activity for the purpose of identifying if a monitored elderly person has fallen or is experiencing a health problem is addressed in [3]. With regard to the mentioned issues in the relevant literature [4], they represent various techniques for detecting the activities of people from videos designed up to 2019. Additionally, Ref. [5] studies if monitoring videos and the content of videos can be used to detect whether a video is suitable for minors. A novel approach for generic visual vocabulary learning is proposed. Such methods require databases of various human activities. An equally interesting approach is presented in [6], where a systematic framework for recognizing realistic actions from videos \"in the wild\" is used. The topic of neural networks is also of interest in a variety of industries not only in terms of its application to video classification but also its ability to address problems related to sound, for which time continuity is also a concern [7]. 3D convolutional neural network (3 DCNN) is a powerful and effective model utilizing spatial-temporal features, which is why they also use it in the article [8] for gesture recognition. In paper [9], proposed a novel deep learning architecture for efficient automatic hand sign language recognition using also 3D Convolutional Neural Network (3 DCNN) from RGB input videos. The combination of 3 DCNN and ConvLSTM is proposed in [10] and used on human action recognition. In [11], is analysed the performance of a 3 DCNN architecture for hand gesture recognition as in the article [8], however in an unstructured scenario. The major contribution [12] is proposed a novel 3 DCNN powered model for scene classification in drone surveillance. In paper [13] proposed deep features of mobile videos are extracted by an exponential linear units-3D convolutional neural network for representing video. In work [14] is studied the ability of state of the art video CNNs including 3D ResNet, 3D ResNet, and I3D for detecting manipulated videos. In [15] presents models based on Convolutional Neural Network (CNN) for problem of classifying videos under the classses as violence and non-violence. Authors in paper used RLVS dataset. Authors in article [16] proposed graph-based framework to learn high-level interaction between object and people, which called Action Detection. In [17] proposed metods to detect violence using Mobil Neural Architecture Authors used Convolutional Long Short-term Memory (ConvLSTM) to extract spatiotemporal features in the video.