YOLO and ChatGPT for Video Summarization and Understanding: Python Program

Introduction

Today's experiment aims to test if YOLOv8 can be used for automated video understanding. Video understanding is the process of analyzing video content to make sense of it, preferably automatically. This involves tasks such as object detection, action recognition, and scene understanding. By leveraging deep learning models, we can automate this process.

The plan is to extract frames from a video and use YOLOv8 to detect and label objects in each frame. YOLO, which stands for "You Only Look Once," is a state-of-the-art object detection system. YOLOv8, in particular, is renowned for its speed and accuracy, making it suitable for various video understanding tasks.

Given an input video, the program will produce two output files: the same video but with bounded boxes around detected objects and a text file containing which frame has which object.

Methodology

We will check if YOLOv8's detected objects make sense to ChatGPT and whether ChatGPT's response makes sense to us. The test will involve two videos: a homemade video of a backyard barbecue and a downloaded video from Pexels showing a street view of London.

I'll provide both videos and the code on a GitHub page, which takes a video as input and generates another video with detected objects labeled. The program also generates a text file that lists detected objects per frame.

Test 1: Backyard Barbecue Video

Here's the result of the backyard barbecue video:

Correct Detections:
- I am detected as a person.
- A chair is detected as a chair.
Incorrect Detections:
- Corn on the cob detected as a hot dog and banana.
- Drumsticks labeled as donuts and pizza.
- Parts of the cooking grate detected as knives.
- Flames looked like pieces of carrot.

This shows several incorrect detections, primarily because the model was pre-trained and not fine-tuned with new data.

Here is an excerpt from the text file generated by my program. I uploaded this text file to ChatGPT and asked it to explain the event of the video. The description it provided was:

"The video appears to depict a backyard picnic or outdoor gathering where food is being shared and consumed. The presence of multiple people, dining tables, and a variety of foods supports this conclusion. The continuous appearance of dining-related objects, such as bowls, cups, and utensils, further reinforces the setting as a picnic or similar outdoor social event."

Even though YOLOv8 made mistakes in detecting objects, ChatGPT was able to infer that it's a backyard picnic or similar social event, which is impressive.

Test 2: Pexels London Street View Video

YOLOv8 performed very well with this video:

Detected cars correctly.
Detected buses correctly.
Incorrectly labeled a chimney as a traffic light.

Just like the previous test, the program generated a text file listing all detected objects. For the sake of brevity, I'm not going to demonstrate the ChatGPT explanation for this file.

Conclusion

The program did better with the street view video compared to the barbecue video, primarily because the street view video contained objects that the YOLO model had seen before. To improve the model for complex scenes like the barbecue video, training with more data would be necessary, although this would be expensive.

For those searching for a video understanding mechanism requiring no additional training, the next video will explain a mind-blowing technique for handling complex content.

By the way, the code for this video and the upcoming one will be available on GitHub. Stay tuned!

Keywords

YOLOv8
Video Understanding
Object Detection
Deep Learning
ChatGPT
Python Program
Scene Analysis
Outdoor Gathering
Training Model
Street View Video

FAQ

Q1: What is video understanding? A1: Video understanding is the process of analyzing video content to make sense of it automatically, including tasks like object detection, action recognition, and scene understanding.

Q2: What is YOLOv8? A2: YOLOv8 stands for "You Only Look Once" and is a state-of-the-art object detection system known for its speed and accuracy.

Q3: How does the program handle video frames? A3: The program extracts frames from a video and uses YOLOv8 to detect and label objects in each frame.

Q4: What are the outputs of the program? A4: The program generates two output files: a video with bounded boxes around detected objects and a text file listing detected objects per frame.

Q5: How did YOLOv8 perform in different video tests? A5: YOLOv8 performed better with the street view video compared to the more complex backyard barbecue video.

Q6: Can YOLOv8 be improved for better detection? A6: Yes, YOLOv8 can be made smarter by training it with more data, although this process can be expensive.

Q7: What role does ChatGPT play in this process? A7: ChatGPT interprets the text file generated by the program to provide an overall understanding of the video content.