PERFORMANCE IMPROVEMENT OF SMART SURVELLIANCE CAMERA USING MODIFIED CNN TECHNIQUE

Abstract

Big data applications are consuming most of the space in industry and research area.
Among the widespread examples of big data, the role of video streams from CCTV
cameras is equally important as other sources like social media data, sensor data,
agriculture data, medical data and data evolved from space research. Surveillance
videos have a major contribution in unstructured big data. CCTV cameras are
implemented in all places where security having much importance. Manual
terms in different contexts like theft identification, violence detection, chances of
explosion etc. In crowded public places the term security covers almost all type of
abnormal events. Among them violence detection is difficult to handle since it
involves group activity. The anomalous or abnormal activity analysis in a crowd
video scene is very difficult due to several real world constraints. The paper includes
a deep rooted survey which starts from object recognition, action recognition, crowd
analysis and finally violence detection in a crowd environment. Majority of the papers
reviewed in this survey are based on deep learning technique. Various deep learning
methods are compared in terms of their algorithms and models. The main focus of this
survey is application of deep learning techniques in detecting the exact count,
involved persons and the happened activity in a large crowd at all climate conditions.
Paper discusses the underlying deep learning implementation technology involved in
various crowd video analysis methods. Real time processing, an important issue
which is yet to be explored more in this field is also considered. Not many methods
are there in handling all these issues simultaneously. The issues recognized in existing
methods are identified and summarized. Also future direction is given to reduce theobstacles identified. The survey provides a bibliographic summary of papers from
ScienceDirect, IEEE Xplore and ACM digital library.