Detection of Abnormality in CCTV Footage to Tackle Insecurity in Nigeria using Computer Vision

Local Chapter Abuja, Nigeria Chapter

Coordinated byNigeria ,

Samuel Nnitiwe Theophilus

Status: Completed

Project Duration: 17 Jan 2023 - 21 Jan 2023

Open Source resources available from this project

Github Repository Project Management Folder

Project background.

The cases of child abuse, domestic violence, theft, kidnapping, and terrorist attacks have significantly increased in Nigeria. Although efforts are being made to install surveillance systems in homes and communities, most of these systems record surveillance footage but are unable to detect abnormal behavior in real-time. Hence most criminal cases are only discovered after they have occurred. This project proposes deep learning as a solution to this challenge.

The problem.

Surveillance systems in offices, streets, or homes should be able to detect abnormalities in the environment in real-time to prevent crimes from happening or stop ongoing criminal activities. This preventive approach to solving crime will greatly decrease the loss/ destruction of properties and prevent fatal injuries/ loss of life in extreme cases. Using AI, we can alert people of danger in time and thereby improve the security conditions in the country.

Project goals.

1. Source for labeled surveillance video datasets for crime/ abnormalities 2. Prepare data and develop fast deep-learning anomaly detection models for real-time usage 4. develop an interface to deploy a successful model for possible integration with surveillance systems (with alert capabilities).

Project plan.

  • Week 1

    Data Collection. The Team will focus on sourcing datasets for training and evaluating trained models.

  • Week 2

    Data Cleaning. The Team will prepare the data collected for training.

  • Week 3

    Data Analysis. The team will perform EDA and begin explore algorithms suitable for solving the given problem.

  • Week 4

    Modeling. The Team will train models and evaluate performance.

  • Week 5

    Deployment. The Team will consider alternative interfaces to deploy the trained model for use.

Learning outcomes.

Image & Video Processing, Anomaly Detection, Object Detection.

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