omegamax10 / yolov8-object-detection-tracking-image-segmentation-pose-estimation Goto Github PK

Dear Developers,

I hope this message finds you well. I am reaching out to discuss an intriguing enhancement opportunity for the YOLOv8 Object Detection and Tracking application, particularly in the realm of adaptive real-time object tracking under varied lighting conditions.

Having perused your GitHub repository and extensively tested the application, I am thoroughly impressed with its capabilities and performance. However, I noticed that the application's object tracking efficiency can notably fluctuate in diverse lighting scenarios, especially in environments with dynamic lighting changes or in low-light conditions. This observation is particularly evident in the Live Stream Tab, where real-time performance is crucial.

Given the importance of consistent and accurate object tracking for a multitude of applications, ranging from security surveillance to traffic monitoring, addressing this issue could substantially enhance the utility and robustness of the application.

Suggested Enhancement:
I propose the integration of an adaptive lighting algorithm that dynamically adjusts the tracking parameters based on the detected lighting conditions. This could involve:

1. Implementing a pre-processing step to assess the lighting condition of each frame or video segment.
2. Adjusting the object detection and tracking parameters, such as contrast, brightness, and threshold values, in real-time based on the lighting assessment.
3. Optionally, integrating an AI-based enhancement model that could improve the clarity and visibility of objects in low-light conditions.

Potential Benefits:

• Improved accuracy and consistency in object tracking across varying lighting conditions.
• Enhanced performance in low-light environments, expanding the application's usability in scenarios like night-time surveillance.
• Increased robustness and reliability, particularly for real-time applications and live stream processing.

I believe this enhancement could mark a significant stride in the application's evolution, further solidifying its position as a leading tool in the field. I look forward to your thoughts on this suggestion and am keen to discuss this further if it aligns with your development roadmap.

Thank you for your time and consideration.

Best regards,
yihong1120

, I am trying to work on offline video tracking

Hi, Thank you so much for such a nice work.

I have ran the repo successfully and for predicting images it works fine, but wenever i try to predict on videos or live streaming it give me error as under:

output_video / 1.mp4/ model.json not found

please guide me in this regard.

Hi! Can you update the script to work with the latest version of yolov8?

Thanks!

Any ways to do this? Seems that the json result is not exposed via http?