Video Action Recognition Using HOG-LDA and Hidden Markov Models
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Solution Overview
Problem
Conventional video classification methods ignore localized spatial and temporal information, leading to poor performance in identifying actions within videos, as they rely on global metadata rather than detailed pose and transition analysis.
Innovation Solution
The implementation of Histogram of Oriented Gradients Linear Discriminant Analysis (HOG-LDA) templates and Localizer Hidden Markov Models (HMMs to learn poses and transitions within videos, using bounding boxes to classify actions by training models on exemplar templates and inferring discrete hidden states for improved localization and recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If global metadata is used for video classification, then the classification process is simple, but the action localization precision is poor
Solution Approach 1:
The patent segments the video into individual frames and further segments each frame into multiple candidate bounding boxes. Instead of treating the entire video as a single unit with global metadata, the system divides it into manageable parts (frames and bounding boxes) that can be individually analyzed for action localization. This segmentation enables precise identification of action regions while maintaining computational feasibility.
2Measurement precision
If HOG-LDA templates and Hidden Markov Models are used to model poses and transitions, then the action localization precision is improved, but the device complexity increases
Solution Approach 1:
The patent employs Hidden Markov Models to capture the dynamic nature of action sequences. The HMM models the temporal transitions between different poses and actions across video frames, allowing the system to understand how actions evolve over time. This dynamic modeling approach significantly improves action localization precision by considering temporal context, while the probabilistic framework provides a mathematically tractable way to handle the complexity.
3Device complexity
If conventional manual classification or global metadata is used, then the system complexity is low, but the localized spatial and temporal information is lost
Solution Approach 1:
The patent transitions from global video-level classification to frame-level and bounding-box-level analysis by introducing spatial (bounding box coordinates) and temporal (frame sequences) dimensions. The HOG-LDA templates operate on local image patches within bounding boxes, extracting localized spatial features. The Hidden Markov Models then add the temporal dimension by modeling sequences of these localized features across frames, thereby recovering and utilizing localized spatial and temporal information that would be lost in global classification approaches.
Data Source
AI summary
This disclosure generally relates to systems and methods that facilitate employing exemplar Histogram of Oriented Gradients Linear Discriminant Analysis (HOG-LDA) models along with Localizer Hidden Markov Models (HMM) to train a classification model to classify actions in videos by learning poses and transitions between the poses associated with the actions in a view of a continuous state represented by bounding boxes corresponding to where the action is located in frames of the video.