Concurrent Video Annotation and Tracking for Replay Latency
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Solution Overview
Problem
Existing video annotation processes for identifying individuals of interest in video content, such as sporting events, are time-consuming and prone to latency issues, which can degrade the viewer experience for instant replays.
Innovation Solution
Concurrent execution of annotation and tracking tasks allows for the initiation of annotation without waiting for tracking to complete, with a 2-stage annotation process for adjusting visualization emphasis objects, reducing latency by 30-50% compared to traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is performed after tracking completes, then annotation accuracy can be ensured, but processing time and latency increase significantly
Solution Approach 1:
The system performs tracking operations in advance to generate bounding boxes before the annotation phase begins. These pre-computed tracking results are then reused during annotation, eliminating the need to wait for tracking to complete before starting annotation. This preliminary action reduces overall processing latency while maintaining annotation accuracy through the 2-stage process.
Solution Approach 2:
The annotation process is divided into two independent stages: Stage 1 performs initial annotation using pre-computed tracking data, and Stage 2 performs refinement if needed. This segmentation allows the first stage to proceed without waiting for complete tracking results, reducing latency while the second stage ensures accuracy through refinement.
2Stability of the object's composition
If annotation tasks wait for tracking completion before starting, then data consistency is maintained, but productivity decreases due to sequential processing
Solution Approach 1:
Tracking operations are executed in advance to produce bounding box data before annotation begins. This preliminary computation ensures that when annotation starts, consistent tracking data is already available, maintaining data consistency without requiring annotation to wait for tracking completion.
Solution Approach 2:
The system introduces an intermediary data structure (pre-computed tracking results with bounding boxes) that serves as a bridge between tracking and annotation operations. This intermediary allows annotation to proceed independently once tracking data is prepared, enabling parallel processing while maintaining consistency through the use of standardized intermediate representations.
Data Source
AI summary
Techniques for reducing the latency of annotating a replay video segment may include receiving a video segment with content involving multiple individuals. An annotation task is performed concurrently with a tracking task. The annotation task receives annotation data to indicate which of the individuals is an individual of interest in a subset of frames of the video segment, and the tracking task tracks the individuals in the video segment by generating bounding objects corresponding to the individuals. The annotation data can be associated with the bounding objects to detect a bounding object for the individual of interest, and a visualization emphasis object is generated based on the detected bounding object in a replay video segment to identify the individual of interest.