论文标题
可疑检测
Suspicious and Anomaly Detection
论文作者
论文摘要
在这个项目中，我们提出了一个CNN架构来检测异常和可疑活动。为该项目选择的活动正在公共场所开展，跳跃和踢球，并在公共场所携带枪支，蝙蝠和刀。通过训练有素的模型，我们将其与Yolo，VGG16，VGG19等先前的模型进行了比较。然后实现训练有素的模型进行实时检测，并使用了。训练有素的.H5模型的TFLITE格式以构建Android分类。
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.
