Deep Neural Networks are Really Undefeatable for Human Conflicting and Nonconflicting Event Detection Human activity analysis has been really vital nowadays for the notification of any threats to the system as well as human. This paper aims to compare conventional AI approaches with recent deep learning approach for the conflicting and Nonconflicting human activity detection. In this paper, we propose the system for the detection of Conflicting human activities namely; Kicking, Punching and Pushing. These systems are developed with AI conventional approaches and Deep Neural Networks. Classifying various activities only into two subgroups of activities based on their intrinsic aggression is really a challenging task. The proposed system effectively uses the wellknown features namely contour and optical flow. System is tested and validated with the wellknown dataset using traditional AI approaches specifically Hidden Markov Model and Support vector machine. We obtain very good accuracy with SVM with polynomial kernel. However, Convolutional Neural network is found outperforming compared to traditional AI approaches and achieves the peak accuracy.

Human Behavior Recognition from Multiview Videos Abstract   With the proliferation of deep learning techniques, a significant number of applications related to home care systems have emerged recently. In particular, detecting abnormal events in a smart home environment has been extensively studied. In this paper, we adopt deep learning techniques, including convolutional neural networks (CNNs) and long shortterm memory (LSTM) networks, to construct deep networks to learn the longterm dependencies from videos for human behavior recognition in a multiview framework. We adopt two cameras as our sensors to efficiently overcome the problem of occlusions and contour ambiguity for improving the accuracy performance of the multiview framework. After performing a series of image preprocessing on the raw data, we obtain human silhouette images as the input to our training model. In addition, because realworld datasets are complicated for analysis, labeling data is time consuming. Therefore, we present an image clustering method based on a stacked convolutional autoencoder (SCAE), which generates clustering labels for autolabeling. Finally, we set up our experimental environment as a normal residence to collect a large dataset, and the experimental results demonstrate the novelty of our proposed models.