Intelligent Video Surveillance

Surveillance security is a very tedious and time-consuming job. In this project, we will build a system to automate the task of analyzing video surveillance. We will analyze the video feed in real-time and identify any abnormal activities like violence or theft.

Project Title

Intelligent Video Surveillance

Project Area of Specialization

Artificial Intelligence

Project Summary

Surveillance security is a very tedious and time-consuming job. In this project, we will build a system to automate the task of analyzing video surveillance. We will analyze the video feed in real-time and identify any abnormal activities like violence or
theft.

Project Objectives

We propose an initial approach to systems designed for knife and firearm detection in images, respectively. our motivation is to solve the problem of knife or firearm recognition in frames from camera video sequences. The aim of these approaches is to provide the capability of detecting dangerous situations in real life environments, e.g., if a person equipped with a knife or firearm starts to threaten other people. The algorithms are designed to alert the human operator when an individual carrying a dangerous object is visible in an image.

Project Implementation Method

We have generally seen deep neural networks for computer vision, image classification, and object detection tasks. In this project, we have to extend deep neural networks to 3-dimensional for learning spatio-temporal features of the video feed.

For this video surveillance project, we will introduce a spatio temporal autoencoder, which is based on a 3D convolution network. The encoder part extracts the spatial and temporal
information, and then the decoder reconstructs the frames. The abnormal events are identified by computing the reconstruction loss using Euclidean distance between original and reconstructed batch.

The frame work of the system starts with the acquiring of video images by means of camera and pre-processing has to be done on them for enhancing the quality of frames in the sequences. The video frames have a lot of noise due to camera, illumination and reflections etc. This can be removed and quality of images can be enhanced with the help of
preprocessing stages. The suitable steps should be carried out in this stage.

The next stage is motion segmentation which separates
foreground images from background images and it is followed by Object classification, Tracking and Human pose modeling. At the end, the activity analysis will be processed.

Benefits of the Project

As the model is trained to identify any theft activity or weapon, an alarm would be called on and respectively an
immediate image would be captured and will be forwarded via cloud to a specific/custom email.

The proposed work drastically reduces the crime rate and it also provide a higher level security in certain areas and it will reduce the time required to catch the criminal and it will aslo reduce human effort.

Technical Details of Final Deliverable

Final deliverable project will be composed of four technical parts:

1- Camera: It will detect any kind of weapon and will immediately capure the image during the surveillance. 

2- Alarm: After detection, an alarm will be called on with the help of micro-controller.

3- Cloud: The captured image will be processed to the cloud storage.

4- Mail-to function: The stored image in the cloud will be forwarded via email to the required destination.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

No Poverty, Quality Education, Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Reduced Inequality, Peace and Justice Strong Institutions

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com