Add action recognition solution guide into docs

docs/en/guides/action-recognition.md

@@ -0,0 +1,94 @@
+---
+comments: true
+description: Learn how to recognize actions in real-time using Ultralytics YOLOv8 for applications like surveillance, sports analysis, and more.
+keywords: action recognition, YOLOv8, Ultralytics, real-time action detection, AI, deep learning, video classification, surveillance, sports analysis
+---
+
+# Action Recognition using Ultralytics YOLOv8
+
+## What is Action Recognition?
+
+Action recognition involves identifying and classifying actions performed by objects (typically humans) in video streams. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/), you can achieve real-time action recognition for various applications such as surveillance, sports analysis, and more.
+
+## Advantages of Action Recognition
+
+- **Enhanced Surveillance:** Detect and classify suspicious activities in real-time, improving security measures.
+- **Sports Analysis:** Analyze player movements and actions to provide insights and improve performance.
+- **Behavior Monitoring:** Monitor and analyze behaviors in various settings, such as retail or healthcare.
+
+## Real World Applications
+
+| Surveillance | Sports Analysis |
+| :----------: | :-------------: |
+| ![Surveillance](https://github.com/RizwanMunawar/ultralytics/assets/62513924/surveillance.jpg) | ![Sports Analysis](https://github.com/RizwanMunawar/ultralytics/assets/62513924/sports_analysis.jpg) |
+| Real-time action recognition for enhanced security | Analyzing player movements and actions |
+
+## How to Use Action Recognition
+
+### Installation
+
+Ensure you have the necessary dependencies installed:
+
+```bash
+pip install ultralytics
+pip install torch torchvision transformers
+```
+
+### Example Usage
+
+Here is an example of how to use the `ActionRecognition` class for real-time action recognition:
+
+```python
+import cv2
+from ultralytics import YOLO
+from ultralytics.solutions.action_recognition import ActionRecognition
+
+# Initialize the YOLO model
+model = YOLO("yolov8n.pt")
+
+# Initialize the ActionRecognition class
+action_recognition = ActionRecognition(video_classifier_model="microsoft/xclip-base-patch32")
+
+# Open a video file or capture from a camera
+cap = cv2.VideoCapture("path/to/video/file.mp4")
+assert cap.isOpened(), "Error reading video file"
+while cap.isOpened():
+    success, frame = cap.read()
+    if not success:
+        break
+    # Perform object detection
+    results = model(frame)
+    # Perform action recognition
+    tracks = results.tracks
+    annotated_frame = action_recognition.recognize_actions(frame, tracks)
+    # Display the frame
+    cv2.imshow("Action Recognition", annotated_frame)
+    if cv2.waitKey(1) & 0xFF == ord("q"):
+        break
+cap.release()
+cv2.destroyAllWindows()
+```
+
+### Arguments
+
+- `video_classifier_model`: Name or path of the video classifier model. Defaults to `"microsoft/xclip-base-patch32"`.
+- `labels`: List of labels for zero-shot classification. Defaults to a predefined list.
+- `fp16`: Whether to use half-precision floating point. Defaults to `False`.
+- `crop_margin_percentage`: Percentage of margin to add around detected objects. Defaults to `10`.
+- `num_video_sequence_samples`: Number of video frames to use for classification. Defaults to `8`.
+- `skip_frame`: Number of frames to skip between detections. Defaults to `2`.
+- `video_cls_overlap_ratio`: Overlap ratio between video sequences. Defaults to `0.25`.
+- `device`: The device to run the model on. Defaults to `""`.
+
+### Methods
+
+- `recognize_actions(im0, tracks)`: Recognizes actions based on tracking data.
+- `process_tracks(tracks)`: Extracts results from the provided tracking data and stores track information.
+- `plot_box_and_action(box, pred_label, pred_conf)`: Plots track and bounding box with action label.
+- `display_frames()`: Displays the current frame.
+- `predict_action(sequences)`: Perform inference on the given sequences.
+- `postprocess(outputs)`: Postprocess the model's batch output.
+
+## Conclusion
+
+Using Ultralytics YOLOv8 for action recognition provides a powerful tool for real-time applications in various domains. By following the steps outlined in this guide, you can implement action recognition in your projects and leverage the capabilities of YOLOv8 for enhanced surveillance, sports analysis, and behavior monitoring.

docs/en/solutions/index.md

@@ -29,6 +29,7 @@ Here's our curated list of Ultralytics solutions that can be used to create awes
 - [Parking Management](../guides/parking-management.md) 🚀 NEW: Organize and direct vehicle flow in parking areas with YOLOv8, optimizing space utilization and user experience.
 - [Analytics](../guides/analytics.md) 📊 NEW: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLOv8 for descriptive, predictive, and prescriptive analytics.
 - [Live Inference with Streamlit](../guides/streamlit-live-inference.md) 🚀 NEW: Leverage the power of YOLOv8 for real-time object detection directly through your web browser with a user-friendly Streamlit interface.
+- [Action Recognition](../reference/solutions/action_recognition.md) 🚀 NEW: Explore Ultralytics' Action Recognition solution leveraging YOLO for real-time action detection and video classification. Learn to implement action recognition in your projects using [action_recognition.py](../guides/action-recognition.md).
 
 ## Contribute to Our Solutions