International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 05 | May 2022 www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Intelligent Video Surveillance System using Deep Learning Neha Kardile, Rutuja Deshmukh, Vaibhav Kalhapure, Project Guide: Prof. Devidas Jaybhay Department of Computer Engineering, G.H.Raisoni College of Engineering, Chas, Ahmednagar. -------------------------------------------------------------------***---------------------------------------------------------------------Abstract- Abnormal activity detection plays a very important role in surveillance applications. To capture the abnormal activity of humans without the intervention of the system i.e. automatically captures the video can be implemented. Human fall detection, suddenly jumping down which has an important application in the field of safety and security. Proposed system use for detecting roadside human activities or behavior by using the Probabilistic Neural Network (PNN) method for classifying activities or behavior between training dataset and testing videos. The partitions between classes of normal activities have also been learned using multi-PNNs. recognizing human activity has become a trend in smart surveillance that contains several challenges, such as performing effective detection of huge video data streams, while maintaining low computational complexity. Current activity recognition techniques are using convolutional neural network (CNN) model with computationally complex classifiers, creating hurdles in obtaining quick responses for abnormal activity, so this paper proposes a framework for activity detection. First, we detect abnormal activity with humans in the surveillance stream using an effective CNN model. The detected individual is tracked throughout the video stream via an ultra –fast object tracker called ‘minimum output sum of squared error’ {MOSSE), Next, for each
interested to provide techniques and methods allowing the detection and classification of human activities, and extended now to recognize normal or abnormal activities. The motivation behind the latter is to provide an immediate intervention to preserve the lives of individuals or to ensure them some services they are unable to do by themselves. Being recent and interesting, this field has attracted the attention of several researchers who try to find solutions to the problems faced in studying such types of activities. However, the proposals made for this until now are those used for the recognition of normal human activities with minor modifications. These proposals are still very restricted because of the very limited number of works and surveys in this field. Moreover, they are not efficient and suffer from several limitations and technical difficulties. To this end, we propose in this paper an overview and an analysis of the existing works, to offer the researchers a general view of what exists in this field and to provide them with a tool being a help to them propose new approaches. For this, the manuscript is organized as follows. In the second section, we present a definition of the abnormal activities, their various types, as well as some examples of abnormal activities of a group or a single person. We then discuss in the third section the motivations that led to the advent of this research axis and the development of techniques allowing the analysis and recognition of human activities in general and abnormal activities in particular. The fourth section is devoted to the proposed approaches in the literature for the detection of abnormal activities. In this section, we present for each proposal, the purpose for which it is set up, its different stages, and the means used for its validation. Subsequently, we discuss some aspects affecting or influencing the effectiveness and credibility of the classification of human activities. The sixth section presents the three modes of automatic learning (supervised, unsupervised, and semisupervised). Thereafter, we enumerate the encountered limitations to be taken into consideration to improve the systems of recognition and identification of abnormal activities. Finally, we finish with a conclusion where we summarize our study.
Tracked individual, pyramidal convolutional features are extracted from two consecutive frames using the efficient LiteFlowNet CNN. Finally, a novel deep skip connection gated recurrent unit is trained to learn different temporal changes in the sequence of frames for activity recognition and detection. We finish by the result indicate the efficiency of the proposed technique. Keywords: Recognition, Video cameras, surveillance systems. I.
INTRODUCTION
During these recent years, applications of video surveillance have attracted more and more researchers. Consequently, various types of modeling, as well as several techniques of analysis and detection of human activities, are suggested. Particularly, many pieces of research are involved in the recognition and detection of human activities in general and especially abnormal activities. One important application is the supervision of elderly and disabled people at home in care centers, or hospitals. Recognition of human activities is a recent field that is © 2022, IRJET
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II. RELATED WORK This paper is much work on abnormal behavior detection took a supervised learning approach. Diverse contributions have been made in the development of behavior recognizers for smart building surveillance applications. In automatic roaders, human surveillance, the vehicle or human activities and behaviors are detected and |
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