A.Jayasimha, P.Mallesh, N.Harsha Vardhan, Mohammed Afzal
In both industry and research, big data applications are taking up much of the available space. Video streams from CCTV cameras are one of the most common examples of big data, and they play a significant role alongside data from other sources such as social media, sensors, agriculture, medicine, and space exploration. The contribution of surveillance videos to unstructured big data is significant. CCTV cameras are installed everywhere that security is a top priority. Manual monitoring appears laborious and time-consuming. Different definitions of security can be used to different situations, such as identifying theft, detecting violence, predicting explosions, etc. The word "security" refers to nearly every kind of unusual occurrence in crowded public areas. Among these, as it entails group work, violence detection is challenging to manageDue to a number of practical limitations, analysing anomalous or aberrant activity in a crowd video scene is quite challenging. In-depth surveying is included in the study, starting with object recognition, followed by activity recognition, crowd analysis, and violence detection in a crowd setting. Deep learning techniques constitute the foundation of the majority of the publications evaluated in this study. Algorithms and models used in various deep learning techniques are compared. This survey's primary goal is to use deep learning techniques to the task of precisely counting the number of participants, the individuals involved, and the activities that occurred in a large crowd under any kind of weather. The fundamental deep learning implementation technology used in several crowd video analysis techniques is discussed in this paper.An essential topic that has not yet received enough attention in this area is real-time processing. There aren't many ways to deal with all of these problems at once. The problems discovered with the current approaches are listed and condensed. Future guidance is also provided to lessen the obstacles found..
Article Details
Unique Paper ID: 162410

Publication Volume & Issue: Volume 10, Issue 10

Page(s): 68 - 73
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