arXiv Open Access 2022

Suspicious and Anomaly Detection

Shubham Deshmukh Favin Fernandes Monali Ahire Devarshi Borse Amey Chavan

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Abstrak

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.

Topik & Kata Kunci

cs.CV

Penulis (5)

Shubham Deshmukh

Favin Fernandes

Monali Ahire

Devarshi Borse

Amey Chavan

Format Sitasi

APA MLA BibTeX

Deshmukh, S., Fernandes, F., Ahire, M., Borse, D., Chavan, A. (2022). Suspicious and Anomaly Detection. https://arxiv.org/abs/2209.03576

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓