Suspicious and Anomaly Detection
This work addresses public safety concerns by detecting specific suspicious activities, but it appears incremental as it builds on standard CNN methods without introducing major innovations.
The authors tackled the problem of detecting suspicious and anomalous activities in public places, such as running, jumping, kicking, and carrying weapons, by proposing a CNN architecture. They compared their model with existing ones like YOLO, VGG16, and VGG19, and implemented it for real-time detection and an Android application using a .tflite format.
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.