Cloud Video Tracking via Optical Flow and Histogram Matching
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Solution Overview
Problem
Existing video detection and tracking systems face challenges in accurately detecting and tracking moving objects in outdoor videos with degraded quality due to noisy sources and unstable lighting conditions, often generating false alarms.
Innovation Solution
A cloud-based video detection and tracking system that utilizes optical flow generation and active-learning histogram matching, combined with user-based voice-to-text color feature description and online target color calibration, to improve the accuracy of target detection and tracking by ruling out erroneous data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If optical flow based approaches are used for motion detection, then motion detection capability is improved, but false alarm rate increases due to noisy sources and unstable lighting conditions
Solution Approach 1:
The patent combines optical flow detection with histogram matching of color features to create a hybrid detection system. The optical flow identifies candidate moving regions, while histogram matching verifies them based on color consistency with known targets, thereby reducing false alarms caused by noise and lighting variations.
Solution Approach 2:
The patent introduces histogram matching as an intermediary verification step between optical flow detection and final target confirmation. This intermediary process filters out false detections by checking whether candidate regions match the expected color histogram of the target, thus improving reliability without sacrificing detection capability.
2Productivity
If cloud computing infrastructure is used for parallel processing, then processing speed and real-time performance are improved, but system complexity increases
Solution Approach 1:
The patent divides the video processing task into separate parallel modules deployed on cloud infrastructure: optical flow computation, histogram calculation, and target matching. Each module processes independently and concurrently, achieving real-time performance while keeping individual module complexities manageable.
Data Source
AI summary
A method for detecting and tracking multiple moving targets from airborne video within the framework of a cloud computing infrastructure. The invention simultaneously utilizes information from an optical flow generator and an active-learning histogram matcher in a complimentary manner so as to rule out erroneous data that may otherwise, separately, yield false target information. The invention utilizes user-based voice-to-text color feature description for track matching with hue features from image pixels.