Keypoint Detection for Interactive Activity Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interactive activity recognition between humans and objects is challenging due to complexity and variability in human interactions, requiring accurate localization and classification of spatial-temporal features in image frames.
Innovation Solution
A method involving keypoint detection using machine learning techniques to identify object and human keypoints in video frames, combining them for spatial-temporal feature extraction with a Graph Convolutional Network, and training a classification model for recognizing interactive activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional activity recognition methods are used, then the system is simpler to implement, but the recognition accuracy and ability to handle complex interactive activities deteriorates
Solution Approach 1:
The system segments the interactive activity recognition problem into distinct components: human keypoint detection, object keypoint detection, and interaction recognition. By dividing the complex task of recognizing human-object interactions into separate detection modules for human keypoints, object keypoints, and interaction patterns, the system achieves higher accuracy while managing complexity through modular architecture
Solution Approach 2:
The system introduces keypoint detection as an intermediary layer between raw image frames and activity recognition. By first detecting keypoints on humans and objects separately, then combining these detections to recognize interactions, the system creates a mediating representation that improves recognition accuracy while structuring the complexity in a manageable way
2Measurement precision
If detailed keypoint detection and spatial-temporal feature extraction are performed, then the recognition precision improves, but the computational time and processing complexity increases
Solution Approach 1:
The system performs preliminary keypoint detection on humans and objects separately before combining them for interaction recognition. By pre-detecting and localizing keypoints on individual entities in advance, the system prepares structured data that accelerates the subsequent interaction recognition process, reducing overall processing time while maintaining high precision
Solution Approach 2:
The system segments the feature extraction process into spatial features from human keypoints, spatial features from object keypoints, and temporal features from their interactions over time. This segmentation allows parallel processing of different feature types, improving extraction accuracy while optimizing computational efficiency
Data Source
AI summary
Computer automated interactive activity recognition based on keypoint detection includes retrieving, by one or more processors, a temporal sequence of image frames from a video recording. The one or more processors identify first and second keypoints in each of the image frames in the temporal sequence using machine learning techniques. The first keypoints are associated with an object in the temporal sequence of image frames while the second keypoints are associated with an individual interacting with the object. The one or more processors combine the first keypoints with the second keypoints and extract spatial-temporal features from the combination that are used to train a classification model based on which interactive activities can be recognized.