Data Collection: Gather a dataset of videos containing instances of shoplifting and non-shoplifting activities. Annotate the videos to indicate when shoplifting occurs.
Data Preprocessing: Preprocess the videos to extract individual frames or short video clips (sequences). Resize the frames to a standard size and apply normalization if needed.
Model Selection: Choose a suitable action recognition model. You can use pre-trained models like 3D CNNs (Convolutional Neural Networks) or RNNs (Recurrent Neural Networks) for this task. These models are capable of learning temporal patterns in video data.
Feature Extraction: Use the selected model to extract features from each frame or video clip. These features should capture important information about the actions taking place in the video.
Model Training: Train the action recognition model using the extracted features and corresponding labels (shoplifting or non-shoplifting). Use techniques like transfer learning to leverage pre-trained models and improve training efficiency.
Detection: Apply the trained model to new videos to detect instances of shoplifting. The model should classify each frame or video clip as shoplifting or non-shoplifting based on the learned patterns.
Post-processing: Perform post-processing steps to filter out false positives and improve the accuracy of the detection. You can use techniques like temporal smoothing or voting across multiple frames to make the detection more robust.
Evaluation: Evaluate the performance of your model using metrics like precision, recall, and F1 score. Fine-tune the model and parameters based on the evaluation results to improve performance.
Deployment: Deploy the trained model in a real-world setting, possibly integrating it with existing security systems or CCTV cameras. Monitor the performance of the system and make adjustments as necessary.
Using an action recognition model for shoplifting detection can be effective, but it requires careful data collection, preprocessing, and model training to achieve accurate results.