Dynamic Object Detection for Textual Information Retrieval
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Solution Overview
Problem
Traditional methods for detecting visual cues in streaming videos are computationally expensive and inefficient due to their reliance on brute force object recognition, making real-time applications burdensome, especially when cues are dynamic and vary frame by frame.
Innovation Solution
The system detects static and dynamic objects by leveraging contextual information to identify relative regions of interest, using a fixed-location detection approach that anchors the detection of dynamic visual cues relative to static or more easily identifiable objects, thereby optimizing visual object recognition and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force object recognition algorithms scan the entirety of video frames, then visual cues can be identified, but computational resources and time are significantly increased
Solution Approach 1:
The video frame is divided into multiple regions of interest based on spatial relationships between visual cues. Instead of scanning the entire frame, the system segments and analyzes only relevant regions, reducing computational load while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary detection of anchor visual cues (such as HUD elements or game objects) to establish reference points before searching for associated textual information. This preliminary action narrows down the search space and enables more efficient processing.
2Ease of operation
If fixed-anchor points are used for detection, then processing is simpler, but dynamic visual cues at varying locations cannot be detected
Solution Approach 1:
The system uses dynamic anchor points that are identified through preliminary object detection rather than fixed predetermined locations. The anchor points adapt to the actual positions of visual cues in each frame, enabling the system to handle dynamic content while maintaining a structured detection approach.
Solution Approach 2:
The system introduces an intermediary step of detecting anchor visual cues (such as game objects or HUD elements) that serve as reference points. These intermediaries bridge the gap between fixed detection methods and dynamic content, allowing the system to locate textual information relative to these anchor points.
3Reliability
If the entire video frame is analyzed for visual cues, then all cues are detected, but computational resources are excessively consumed
Solution Approach 1:
The system applies different detection strategies to different regions of the video frame. Regions containing anchor visual cues receive focused analysis to identify associated textual information, while other regions are processed with reduced intensity or skipped entirely, optimizing resource allocation.
Solution Approach 2:
The system performs partial analysis by focusing only on regions likely to contain the target visual cues based on their spatial relationship to anchor points. This partial action approach detects all relevant cues while avoiding unnecessary analysis of irrelevant frame portions.
Data Source
AI summary
In various examples, frames of a video may include a first visual object that may appear relative to a second visual object within a region of the frames. Once a relationship between the first visual object and the region is known, one or more operations may be performed on the relative region. For example, optical character recognition may be performed on the relative region where the relative region is known to contain textual information. As a result, the identification of the first visual object may serve as an anchor for determining the location of the relative region including the second visual object—thereby increasing accuracy and efficiency of the system while reducing run-time.