Key point labeling is a task in the field of computer vision, which refers to marking the key points of a specific target in an image or video sequence. These key points are usually important feature points or contour points of the target, including but not limited to human joints, Facial feature points, vehicle parts, etc. By marking key points, it can provide important information support for subsequent tasks such as target tracking, pose estimation, and action recognition. Key point annotation usually requires professional tools or machine learning models, and requires manual review and correction.
Keypoints can represent various aspects of an image, such as corners, edges, or specific features, depending on the application. For example, in facial recognition, they can label eyes, noses, and mouths, while in human pose estimation, keypoints can represent body joints.
Using keypoints is one of the most accurate labeling methods. They are a great way to prepare training data for:
Facial expression recognition, human and animal pose estimation, navigation and driver behavior analysis, livestock behavior tracking, gesture recognition, activity recognition, robotics and manufacturing, video surveillance, motion analysis, 3D reconstruction.
With a dataset containing keypoint annotations, your model can gain a finer-grained understanding of the spatial relationships between different objects or structures in each image. This allows you to solve more complex computer vision tasks and make better predictions.
For keypoint skeletons, each point is unique and represents a specific landmark, joint or edge. On the other hand, polygonal annotations only delineate regions of interest to create instance segmentation masks. We know which parts of the image belong to our main subject, which parts are the background, and that's it.
The structure remains the same, and we can reuse the same keypoint skeleton to annotate multiple images. In contrast, the polygon labels on the right do not contain this information, and the number of points may vary across different images and frames of the dataset. These points are not "key" points - they don't represent anything in particular other than the general shape of the polygon segmentation mask.
When to Use Keypoint Labeling
Keypoint annotation is used in some of the most challenging computer vision tasks. For example, keypoints and keypoint skeletons are crucial for human pose estimation or gesture recognition, as these tasks require more precise and detailed data. Involves predicting the coordinates of keypoints in an image or video frame. A keypoint regression model will predict the exact location of a particular keypoint in that image or frame. This technique is often used in motion tracking along with keypoint detection. Keypoint labeling is also great for analyzing the spatial relationship between multiple objects or particles, such as football players on the field.
Keypoints provide high-quality data, but they require extensive manual annotation. Bounding box and polygon annotations are generally easier to annotate and are often used for simpler computer vision tasks such as basic object detection. While very powerful in the right hands, keypoint-based image annotation presents several challenges. The main ones are accuracy, consistency and scalability.
The main disadvantages of keypoint labeling:
Identifying the exact location of some keypoints is difficult (parts of the object may be occluded or out of frame); human annotators may interpret landmarks differently or label them in slightly different locations; for large datasets Creating keypoint annotations can be time-consuming and labor-intensive. To meet these challenges, it is important to use the right data annotation tools and establish clear labeling guidelines for your keypoints and keypoint skeletons. Additionally, you need to use quality control measures like review stages to ensure the best possible results.
Adding keypoint annotations and reconstructing landmark locations in 3D space using multi-view medical scans is another important use case. To create state-of-the-art models for healthcare, using temporal or 3D data is often the best approach.
The importance of data labeling
1. Improve algorithm performance: Key point annotation can help the algorithm identify and track specific parts or objects, thereby improving recognition rate and tracking accuracy.
2. Improve data quality: Labeling key points can make data more accurate and reliable, and help remove redundancy and noise in data.
3. Speed up training: Marking key points can greatly reduce the amount of data and learning time required for learning models. When keypoints are annotated, the model can learn more quickly what features these "keypoints" represent.
4. Improve human-computer interaction: Annotating key points can improve the performance and user experience of interactive applications. For example, apps that can recognize faces and react to facial expressions.
Jinglianwen Technology conducts daily training for the labeling personnel and quality inspection personnel participating in the key point labeling. The quality inspection personnel are trained by the project supervisor as a whole, and the quality inspection is carried out at the same time as the labeling, and the quality inspection is carried out in real time. All data are subject to sampling inspection by the team leader, inspection by quality inspectors, and inspection by the project manager for final delivery. Among the annotators who perform key point annotation, undergraduate annotators account for 30%, and junior college annotators account for 65%, with high quality.
As a professional data collection and labeling company, Jinglianwen Technology has very rich experience in implementing AI data projects and a complete project management integration process. After years of accumulation, it has 5 labeling bases nationwide and has 1,000 full-time labelers. , which launched its own labeling platform in 2020, covering most of the mainstream labeling tools, supporting the labeling of key information points on faces and human body, which can improve the accuracy of the model by more than 30%, greatly shorten the iteration cycle, and single-time Model training costs can be saved by 30%, key points can be marked well, and customized services can be provided for enterprises according to their actual needs.
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