The document discusses the need for automating suspicious activity detection in video surveillance due to the increasing prevalence of antisocial behavior and the impracticality of constant human monitoring. It proposes the use of machine learning, specifically convolutional neural networks, to analyze video frames and identify suspicious behavior more efficiently. The proposed system aims to streamline the detection process by using a trained model to classify actions in uploaded videos.

1. Venkat Java Projects
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Suspicious Activity Detection
ABSTRACT
With the increasing in the number of anti-social activates that have been taking place, security
has been given utmost importance lately. Many Organizations have installed CCTVs for constant
Monitoring of people and their interactions. For a developed Country with a population of 64
million, every person is captured by a camera 30 times a day. A lot of video data generated and
stored for a certain time duration. A 704x576 resolution image recorded at 25fps will generate
roughly 20GB per day. Constant Monitoring of data by humans to judge if the events are
abnormal is near impossible task as requires a workforce and their constant attention. This
creates a need to automate the same. Also , there is need to show in which frame and which part
of it contain the unusual activity which aid the faster judgment of the unusual activity being
abnormal. This is done by converting video into frames and analyzing the persons and their
activates from the processed frame .Machine learning and Deep Learning Algorithms and
techniques support us in a wide accept to make Possible.
EXISTING SYSTEM:
Video Surveillance system is a collection of video, electronic and wireless components to ensure the
continuousorperiodicvideorecordingformonitoringthe various importantpubliclocations.Due to the
increased crime rate and instable incidents are happening around the world many organizations are
deploying video surveillance systems at their locations with CCTV cameras. Now a day we can notice
these camerasat variouspubliclocationslikebusstops,shoppingmalls,holyplaces,streets,educational
institutes,publicmeetingsetc.The capturedvideodataisuseful topreventthe threat before crime and
becoming a good forensic evidence to identify criminals after crime.
Disadvantages:
PROPOSED SYSTEM

2. Venkat Java Projects
Mobile:+91 9966499110 Visit:www.venkatjavaprojects.com
Email:venkatjavaprojects@gmail.com
In this project we need to detect person behaviour as suspicious or not, now a day’s
every where CCTV cameras are installed which capture videos and store at
centralized server and manually scanning those videos to detect suspicious activity
from human required lots of human efforts and time. To overcome from such issue
author is asking to automate such process using Machine Learning Algorithms.
To automate that process first we need to build training model using huge amount
of images (all possible images which describe features of suspicious activities) and
‘Convolution Neural Network’ using TENSOR FLOW Python module.
Then we can upload any video and then application will extract frames from
uploaded video and then that frame will be applied on train model to predict its
class such as ‘suspicious or normal’.