Semantic-Guided Temporal Activity Detection in Unlabeled Videos
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Solution Overview
Problem
Videos lack effective searchability due to the absence of textual metadata, and manual tagging is inefficient and inconsistent, making it difficult to identify specific activities or actions within unlabeled video content.
Innovation Solution
A self-learning and semi-supervised activity detection system (ADS) that utilizes artificial intelligence and machine learning techniques to detect and classify unseen activities by mapping visual features to semantic features, leveraging semantic similarity between textual terms to enhance the classification of unlabeled videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual tagging is used to add text metadata to videos, then video searchability and subject matter identification are improved, but the process becomes extremely time-consuming and inconsistent
Solution Approach 1:
The system enables videos to automatically generate their own tags and metadata through self-supervised learning. The model processes video content and generates tags without human intervention, allowing the system to serve itself in the tagging task rather than relying on manual human labor
Solution Approach 2:
The patent replaces the mechanical process of manual human tagging with an automated machine learning system. The self-supervised model processes video frames, detects objects and activities, and generates tags algorithmically, substituting human mechanical tagging operations with computational processes
2Difficulty of detecting and measuring
If image recognition techniques are used to identify objects in video frames, then object detection capability is improved, but the system identifies irrelevant background objects and fails to capture video subject matter
Solution Approach 1:
The system segments the video processing task into multiple stages: frame-level object detection, temporal pattern recognition across sequences of frames, and hierarchical tag generation. This segmentation allows the system to distinguish between transient background objects and significant subject matter by analyzing temporal patterns
Solution Approach 2:
The patent adds the temporal dimension to traditional image recognition by analyzing sequences of frames over time. Instead of treating each frame independently, the system processes temporal patterns across multiple frames to distinguish meaningful objects from background noise, effectively moving from 2D spatial analysis to 3D spatio-temporal analysis
3Productivity
If a self-learning activity detection system is implemented to automatically tag videos, then tagging efficiency and consistency are improved, but the system requires computational resources and training data
Solution Approach 1:
The system uses self-supervised learning where the model learns to generate tags by processing its own predictions and comparing them with video content. The model automatically creates its own training signals from unlabeled video data, eliminating the need for external annotated datasets and reducing system complexity
Solution Approach 2:
The system performs preliminary learning on large amounts of unlabeled video data to build general object and activity recognition capabilities before being deployed for specific tagging tasks. This preliminary training phase prepares the model to handle diverse video content without requiring task-specific annotated data
Data Source
AI summary
Disclosed is an activity detection system (“ADS”) that detects, classifies, and isolates previously unseen activities in unlabeled videos based on different previously seen activities in labeled videos and semantic similarity between the unseen and seen activities. The ADS receive a first set of videos that are labeled with a first activity, and may determine a feature set within frames of the first set of videos that represents the first activity. The ADS may receive a second set of videos that are not labeled, and a query for videos of a second activity that is determined to be semantically similar to the first activity. The ADS may provide, in response to the query for the second activity, a particular video from the second set of videos containing the feature set representing the first activity that is semantically similar to the queried for second activity.