Semantic Video Activity Detection for Unlabeled Action Retrieval
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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 videos.
Innovation Solution
A self-learning and semi-supervised activity detection system (ADS) that utilizes AI/ML techniques to detect and classify unseen activities by mapping visual features to semantic features, leveraging semantic similarity between textual terms to enhance classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used to add video metadata, then video searchability and identification accuracy are improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The patent replaces the manual mechanical tagging process with an automated activity detection system that uses machine learning models to analyze video content and generate tags automatically. The system processes video frames, detects activities, and classifies videos without human intervention, thereby eliminating the time-consuming manual tagging while maintaining or improving classification accuracy through consistent algorithmic application.
Solution Approach 2:
The system enables videos to tag themselves automatically by analyzing their own content through the activity detection model. The video processing pipeline extracts features from video frames, detects activities performed by subjects, and generates appropriate tags and classifications autonomously, making the tagging process self-service rather than relying on external human annotators.
2Reliability
If image recognition techniques are used to identify objects in video frames, then object detection capability is improved, but false identification of irrelevant background objects increases
Solution Approach 1:
The patent segments the video analysis process into distinct stages: frame extraction, activity detection, and classification. By dividing the video into sequential frames and analyzing them through a structured pipeline, the system focuses detection on relevant temporal segments rather than treating the entire video as a single static image, thereby reducing background noise and improving the signal-to-noise ratio for activity identification.
Solution Approach 2:
The system transitions from static image recognition to dynamic video analysis by processing sequences of frames over time. The activity detection model analyzes temporal patterns and motion across multiple frames, enabling it to distinguish between stationary background objects and dynamic activities of interest, thereby improving reliability while filtering out irrelevant background elements.
3Measurement precision
If a large number of video frames are analyzed to improve activity detection accuracy, then detection precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential features from video frames that are relevant to activity detection, rather than processing all pixel data from every frame. The system identifies and extracts key visual features such as motion patterns, object trajectories, and activity-specific characteristics, thereby reducing computational complexity while maintaining detection precision by focusing on discriminative features.
Solution Approach 2:
The system applies partial action by analyzing a strategically selected subset of frames rather than every single frame in the video. By sampling key frames at critical moments or using frame skipping strategies, the system achieves sufficient detection precision with reduced computational load, avoiding the excessive processing that would result from analyzing all frames in detail.
4Stability of the object's composition
If consistent tagging is achieved through standardized protocols, then classification consistency is improved, but flexibility in handling diverse video content decreases
Solution Approach 1:
The patent implements a universal activity detection model that can handle multiple video types and content categories through a single standardized pipeline. The system is designed to process diverse video content (sports, news, entertainment, etc.) using the same core detection algorithms and classification framework, ensuring consistent tagging across different genres while maintaining adaptability through configurable parameters and training data diversity.
Solution Approach 2:
The system maintains consistency through standardized processing parameters and algorithms while adapting to diverse content by adjusting training data compositions and model configurations. The core detection pipeline remains consistent and standardized, but the system can be retrained or fine-tuned with different datasets to adapt to specific video domains, thereby achieving both consistency in methodology and versatility in application.
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.