Enhancing YOLOv8 to natively handle video sequences

[Bhavay-2001]

Hi everyone, I would like to work on the idea of adding video data support for YOLOv8.

Currently, YOLOv8 doesn't handle video data directly and requires models like LSTM or GRU on top of the YOLOv8 outputs to include the temporal dimension.

I would like to discuss on how we can add support for video data in the YOLOv8 model starting from data loading and processing going up till output and detections.

I don't have any suggestions myself but would love to discuss on possible suggestions or advice. I would love to open a PR for the same as well.

Thanks

[pderrenger]

Hi there! Great to hear that you're interested in enhancing YOLOv8 with native video sequence handling. 🎥

Indeed, integrating temporal data directly into the YOLOv8 architecture could potentially improve detection performance in video streams by leveraging the temporal continuity between frames.

A good starting point might be to explore how existing models integrate RNN layers (like LSTM or GRU) with CNN outputs. For YOLOv8, you could consider a similar approach where the convolutional features from consecutive frames are fed into recurrent layers to capture temporal dependencies.

For data loading, you might need to modify the existing dataloaders to batch video frames rather than single images. Regarding the output and detections, ensuring the model can maintain object identities across frames would be crucial.

If you're ready to start experimenting, feel free to fork the repository and work on these enhancements. Once you have a working prototype, opening a PR would be the best way to discuss further improvements and possibly integrate them into the main branch.

Looking forward to seeing what you come up with! 🚀

[Bhavay-2001]

Hi Paula, thanks for your reply. Could you suggest some papers or codes from where I can start exploring about integration of RNN layers with CNN outputs. Any good starting point that can help?

[pderrenger]

Hi! Absolutely, a good starting point for integrating RNN layers with CNN outputs is the paper on Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. This paper provides a detailed methodology on combining spatial features from CNNs with temporal features using LSTMs.

For code, you might want to check out implementations of ConvLSTM models available on GitHub, which are often applied in video prediction tasks. These examples can give you a practical insight into how the integration can be achieved.

Happy exploring! 🚀

[Linengyao]

hi, i want to do same work as u, do u have any process now?@Bhavay-2001

[glenn-jocher]

Hi @Linengyao,

It's great to see your interest in enhancing YOLOv8 to natively handle video sequences! This is indeed an exciting area with a lot of potential for improvement and innovation.

Currently, YOLOv8 is designed primarily for image-based object detection, and as you mentioned, handling video data typically requires additional models like LSTM or GRU to capture the temporal dimension. Here are a few steps and considerations to get started on this enhancement:

1. Data Loading and Processing:

   • Extend the data loader to handle video files. This involves reading video frames and possibly converting them into a suitable format for YOLOv8.
   • Implement a mechanism to handle video streams efficiently, ensuring that frames are processed in real-time or near real-time.

2. Model Architecture:

   • Integrate temporal models (e.g., LSTM, GRU) with YOLOv8 to capture temporal dependencies between frames.
   • Consider how to pass the output of YOLOv8 (bounding boxes, class scores) into the temporal model.

3. Training Pipeline:

   • Modify the training pipeline to support sequences of frames instead of individual images.
   • Ensure that the loss functions and optimization processes are compatible with the new architecture.

4. Inference and Output:

   • Adapt the inference process to handle video input, ensuring that the model can process and output detections for each frame in a video sequence.
   • Implement post-processing steps to handle the temporal aspect of detections, such as tracking objects across frames.

Here's a basic example to get you started with video frame extraction and processing:

import cv2
from ultralytics import YOLO

# Load a pretrained YOLOv8 model
model = YOLO("yolov8n.pt")

# Open the video file
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    # Run YOLOv8 inference on the frame
    results = model(frame)

    # Visualize the results on the frame
    annotated_frame = results[0].plot()

    # Display the annotated frame
    cv2.imshow("YOLOv8 Inference", annotated_frame)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

Feel free to open a PR once you have a working prototype or if you need further assistance. The community and the Ultralytics team are here to help you refine and integrate your enhancements. Good luck, and we're excited to see what you come up with! 🚀