Video Object Tracking Using Multi-Frame Latency Fusion
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Solution Overview
Problem
Conventional text detection and recognition systems face challenges in accurately tracking moving text in video streams, leading to high false alarm rates and suboptimal performance due to their reliance on single-image based localization and recognition methods, which do not effectively utilize temporal information.
Innovation Solution
The proposed technique performs object tracking and recognition in video streams by utilizing multi-frame latency to combine tracking and processing results, employing Kalman filters and data association methods to improve text localization and recognition accuracy, and reduces false alarms by leveraging temporal information across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-image based localization and recognition methods are used, then device complexity is reduced, but text detection accuracy and reliability deteriorate due to high false alarm rates and inability to track moving text effectively
Solution Approach 1:
The system segments the text detection and tracking problem into distinct processing stages: object detection in individual frames, temporal tracking across frames, and result fusion. This segmentation allows each component to be optimized independently while improving overall detection accuracy through multi-frame analysis
Solution Approach 2:
The system transitions from analyzing single 2D images to processing 3D spatiotemporal video data by adding the time dimension. This enables the system to track moving text across multiple frames and distinguish true text detections from false alarms using temporal consistency
2Reliability
If single-frame processing is used, then processing speed is improved, but reliability deteriorates due to high false alarm rates
Solution Approach 1:
The system performs preliminary object detection on individual frames first, then applies temporal tracking as a refinement step. This preliminary action allows fast single-frame processing to identify candidate regions, which are then verified across multiple frames to reduce false alarms
Solution Approach 2:
The tracking module provides feedback to the detection process by using detected objects in previous frames to guide search in current frames. This feedback mechanism reduces false alarms by requiring temporal consistency while maintaining processing efficiency through focused search regions
3Loss of time
If multi-frame latency is reduced, then real-time performance is improved, but text recognition accuracy may deteriorate due to insufficient temporal information utilization
Solution Approach 1:
The system maintains continuous tracking of detected objects across frames, ensuring that useful detection results are preserved and propagated through the video stream. This continuity allows the system to achieve low latency by not re-detecting objects in every frame while maintaining high recognition accuracy through persistent temporal tracking
Data Source
AI summary
A method includes tracking an object in each of a plurality of frames of video data to generate a tracking result. The method also includes performing object processing of a subset of frames of the plurality of frames selected according to a multi-frame latency of an object detector or an object recognizer. The method includes combining the tracking result with an output of the object processing to produce a combined output.