Video Object Detection Using Frame Subset Selection
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Solution Overview
Problem
Existing video-based security systems face inefficiencies in selectively converting video information into electronic output files, particularly in detecting pertinent video content and generating relevant output files, as they often process entire video frames, which is resource-intensive and unnecessary for object and motion detection.
Innovation Solution
A system and method that utilize hardware processors to select a subset of video content, perform multiclass object detection, and motion detection on the selected subset, generating electronic output files in a raster-graphics format, focusing on detected objects and movements, thereby reducing processing requirements and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire video content is processed for object and motion detection, then detection accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The video content is segmented into individual video frames, and a subset of these frames is selected for processing rather than analyzing every frame. This segmentation approach maintains detection accuracy by ensuring sufficient temporal sampling while reducing processing time and computational resource requirements.
Solution Approach 2:
Instead of processing the complete video content, the system performs partial action by selecting and processing only a subset of video frames. This partial processing approach is sufficient for effective object and motion detection while significantly reducing the computational burden and processing time.
2Reliability
If all video frames are analyzed for object detection, then object detection completeness is improved, but computational resources and processing power increase
Solution Approach 1:
The video content is divided into discrete frames, and the system processes only a selected subset of these frames for object detection. This segmentation strategy ensures that detection completeness is maintained through adequate temporal sampling while reducing computational resource requirements.
Solution Approach 2:
The system performs partial processing by analyzing only a subset of video frames rather than all frames. This approach provides sufficient object detection completeness for security applications while significantly reducing the computational power and processing resources needed.
3Measurement precision
If video content is processed in full resolution and format, then detection precision is improved, but processing speed decreases
Solution Approach 1:
The video content is segmented into individual frames, and processing is performed on a subset of these frames rather than the complete video sequence. This segmentation approach maintains detection precision through adequate temporal sampling while improving processing speed by reducing the total number of frames analyzed.
Solution Approach 2:
The system applies partial processing by selecting and analyzing only a subset of video frames at full resolution. This approach maintains sufficient detection precision for identifying objects and motion while significantly improving processing speed and overall system productivity.
Data Source
AI summary
Systems and methods for converting video information into electronic output files are disclosed. Exemplary implementations may: obtain video information defining one or more videos; select a subset of the visual content included in the video content of the particular video; perform object detection to detect objects in the selected subset; perform motion detection to detect a movement for one or more of the detected objects in the selected subset, responsive to the object detection detecting one or more detected objects; and generate and store an electronic output file, responsive to the detection of the movement.