Video Annotation Interface for Action Segmentation
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Solution Overview
Problem
Current security systems require manual annotation of video metadata for human action identification, which is time-consuming and inefficient due to varying camera positions and limited coverage, and existing annotation tools are not intuitive, making the process costly and proprietary.
Innovation Solution
A system utilizing 2D and 3D computer vision techniques to generate a 2D dataset with arbitrary viewpoints from multiple-angle cameras, providing a user interface for video annotation tools that enables efficient annotation and generation of training data by parsing videos into segments and associating them with objects and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of video metadata is performed to identify human actions, then action identification accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The video is divided into multiple segments based on detected actions, and annotation tracks are created for each segment. This allows annotators to work on specific portions of the video rather than the entire video, reducing overall annotation time while maintaining accuracy through focused attention on segmented portions.
Solution Approach 2:
The system automatically detects actions in the video and generates annotation tracks before manual annotation begins. This preliminary automated detection provides a head start, reducing the time annotators need to spend on basic identification tasks while they focus on refining and verifying the detected actions for accuracy.
2Ease of operation
If conventional annotation tools are used to review human actions in videos, then annotation capability is provided, but user effort and time requirements increase
Solution Approach 1:
The system introduces an intermediary automated action detection component that processes video content and generates preliminary annotation tracks. This intermediary tool bridges the gap between raw video data and manual annotation, providing annotators with pre-processed information that reduces their effort and time requirements while maintaining annotation quality.
Solution Approach 2:
The system creates copies of the video segmented into action-specific portions, with each segment presented in its own annotation track. This allows annotators to work with simplified copies of the video content rather than the full video, reducing cognitive load and time requirements while maintaining the ability to annotate with high capability.
3Area of stationary object
If multiple cameras are used to improve video coverage, then coverage area increases, but system complexity and cost increase
Solution Approach 1:
The annotation system is designed to handle videos from multiple cameras universally, with a single interface that can process and annotate content from any camera angle. This multi-functional capability allows the system to work with expanded coverage from multiple cameras without proportionally increasing annotation complexity, as the same annotation tools and processes apply regardless of camera source.
Data Source
AI summary
Implementations generally provide a user interface for video annotation tools. In some implementations, a method includes obtaining at least one video of at least one object performing at least one action displaying one or more portions of the at least one video in a user interface. The method further includes displaying a plurality of annotation tracks in the user interface, where each annotation track of the plurality of annotation tracks is associated with one or more of the at least one object and the at least one action in the at least one video. The method further includes obtaining one or more annotations associated with the at least one video based on the plurality of annotation tracks.