This is Embed-C code written in Arduino IDE for ESP8266 for a "Low-Cost Theft-Detection System using MPU-6050 and Blynk IoT Platform".
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Updated
Oct 22, 2020 - C++
This is Embed-C code written in Arduino IDE for ESP8266 for a "Low-Cost Theft-Detection System using MPU-6050 and Blynk IoT Platform".
GRU Model That uses a sequence of MobileNet Image Features to classify a video clip as class label shoplifting or not shoplifting.
An AI-powered system for automatic shop theft detection using deep learning and computer vision. Supports multiple video classification models (EfficientNet + LSTM, 3D CNN, Transformers, VideoMAE). Uses YOLOv8 to detect people in frames. Deployed with a Django web app for easy video upload, prediction, and annotated output.
AI based website for real time montoring natural resources.
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