Spherical Video Object Detection via Multi-Projection Merging
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Solution Overview
Problem
Manually framing wide field of view videos to identify objects is difficult and time-consuming, especially in spherical videos where determining interesting spatial extents is challenging.
Innovation Solution
The system generates multiple perspective projections of spherical videos, performs object detection in each projection, adjusts detection scores based on proximity to boundaries, filters out redundant detections, and tracks objects across projections to accurately identify and present relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple perspective projections are generated for object detection, then object detection accuracy is improved, but device complexity increases
Solution Approach 1:
The spherical video is divided into multiple perspective projections, each covering a specific spatial extent. Object detection is performed independently on each projection, which simplifies the detection process compared to analyzing the entire spherical video at once. This segmentation approach improves detection accuracy while managing system complexity through modular processing.
Solution Approach 2:
The system transforms the three-dimensional spherical video data into multiple two-dimensional perspective projections. This dimensional transformation allows standard 2D object detection algorithms to be applied effectively, improving detection accuracy while maintaining manageable computational complexity through the use of established 2D processing techniques.
2Manufacturing precision
If manual framing is used to identify objects in wide field of view videos, then framing precision can be achieved, but time consumption increases
Solution Approach 1:
The system performs automated object detection and framing by processing multiple perspective projections independently and merging the results. This self-service approach eliminates the need for manual framing while maintaining high precision through the systematic analysis of multiple views, significantly reducing time consumption compared to manual methods.
Solution Approach 2:
The system performs preliminary object detection in each perspective projection before final framing decisions are made. By pre-identifying objects of interest in multiple projections and their spatial relationships, the system can automatically determine optimal framing without manual intervention, achieving both high precision and efficiency.
3Measurement precision
If object detection is performed in multiple perspective projections, then object detection accuracy is improved, but processing time increases
Solution Approach 1:
The processing is segmented into independent steps: generating multiple perspective projections, performing object detection on each projection separately, and then merging results. This segmentation allows parallel processing of different projections, improving detection accuracy while managing processing time through efficient task distribution and independent computation of each projection.
Data Source
AI summary
A wide field of view video is split into multiple perspective projections, with individual perspective projections providing a two-dimensional view of a spatial extent of the wide field of view video. Object detection is performed within individual perspective projections to determine the placement of the objects within individual perspective projections. The placement of the objects are projected back into the wide field of view video to merge the detections. Redundant detection are filtered out and the remaining detections are used to perform object tracking in the wide field of view video.