How to manage Occlusion, Pose & Viewpoint variation problems in human detection solutions? Ep-12

Check out VisAI Labs blog on "Top 3 Technical Problems In Human/People Detection Solutions."

Link: https://visailabs.com/top-3-technical-problems-in-human-people-detection-solutions/

Also, watch the VisAI Labs podcast on "How to manage Occlusion, Pose & Viewpoint variation problems in human detection solutions?"

Link: https://youtu.be/Ac2IgxT4yYo

In general, AI-based models perform better when trained in a localized and regulated environment. However, the most crucial benefit of using an AI solution is its generalizability.

It can be practiced in a particular environment with helpful data variations and applied with reasonable precision in a different environment. We must train the AI model to learn the features of the target object rather than the features of the environment.

Below are the three major issues we often face in human\people detection solution:

1. Viewpoint variation: As the object’s viewpoints vary, the object’s shape changes drastically, which also changes the object’s features, and this causes the detector trained from one perspective to fail on other viewpoints.

2. Pose variation: Several objects of interest are not rigid bodies; they can be deformed in powerful ways. In the human detection solution, the person can be in different kinds of pose variations like walking, standing, running, sitting, doing any activities, etc. The features of the person in these pose variations vary drastically. The training set must include all these variational inputs to detect all kinds of pose variations in the solution.

3. Occlusion: Occlusion is another major problem for human detection in general. Some scenarios include when two or more persons come too close, or when the object occludes a person in front, or when a chair partially occludes a person. In these scenarios, the object of interest seemingly merges. These scenarios are called occlusion.

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