Abstract
Cash vending machines are ubiquitous and although their technology vouches for its security, they are erratically stormed by the raiders. Albeit the escalating crime counts, the raiders are fleeing from the justice by virtue of evidence lacking. This research work proposes a computer vision based Anti-Raider ATM system. The proposed approach models the image, acquired from the CCTVs against the raider images based on the computer vision and deduces the fact from the MobileNetv2 architecture. Once the model identifies the raider, the image is uploaded to the Google Drive, which serves as evidence for the judicial department. The proposed research is modeled against several optimizers and the result concludes that, among them Adam optimizer has excelled in both computation time and accuracy.
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