Suspicious and Anomaly Detection

TL;DR

This paper presents a CNN-based system for detecting suspicious and anomalous activities in public places, comparing it with existing models and deploying it for real-time Android applications.

Contribution

Introduces a new CNN architecture for anomaly detection and demonstrates its effectiveness compared to existing models in real-time scenarios.

Findings

The CNN model outperforms Yolo, Vgg16, and Vgg19 in detecting suspicious activities.

The model is successfully deployed on Android using tflite for real-time detection.

Effective detection of activities like running, jumping, kicking, and carrying weapons.

Abstract

In this project we propose a CNN architecture to detect anomaly and suspicious activities; the activities chosen for the project are running, jumping and kicking in public places and carrying gun, bat and knife in public places. With the trained model we compare it with the pre-existing models like Yolo, vgg16, vgg19. The trained Model is then implemented for real time detection and also used the. tflite format of the trained .h5 model to build an android classification.