Motion Event Categorization via Video Segmentation
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Solution Overview
Problem
Existing video surveillance systems face challenges in accurately and efficiently identifying and categorizing meaningful motion events within video streams, often producing false positives and missing important events due to inappropriate sensitivity settings, leading to tedious manual review of irrelevant data.
Innovation Solution
A method involving a server system that segments video streams, categorizes motion events using initial and subsequent segments, generates log entries, and updates categorizations based on motion start and end information, with the option to send alerts and adjust categorizations based on confidence scores and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion detection sensitivity is set high, then more motion events are detected, but false positives increase (trivial movements like tree leaves and sunlight shifts are recorded)
Solution Approach 1:
The video stream is segmented into multiple segments based on motion detection results. The system divides the continuous video into discrete segments, analyzing each segment independently to determine whether it contains a meaningful motion event. This segmentation allows the system to filter out trivial movements by evaluating motion patterns across multiple segments rather than reacting to every detected motion immediately.
Solution Approach 2:
The system performs preliminary analysis of video segments before generating alerts or requiring full manual review. By pre-processing and categorizing segments based on motion characteristics, the system can identify and filter out false positives early in the process, preventing trivial movements from triggering unnecessary alerts or review requirements.
2Object-generated harmful factors
If motion detection sensitivity is set low, then false positives are reduced, but important events may be missed
Solution Approach 1:
The system dynamically adjusts its analysis approach based on motion characteristics detected in each segment. Rather than using a fixed sensitivity threshold, the system adapts its evaluation criteria according to the specific motion patterns observed, allowing it to maintain high detection accuracy while minimizing false positives across different scenarios and locations.
Solution Approach 2:
The system uses feedback from segment analysis to refine its motion event identification. By continuously evaluating motion patterns across segments and using the results to adjust subsequent analysis, the system can distinguish between trivial movements and important events more accurately over time, improving reliability without increasing false positives.
3Reliability
If all detected motions are reviewed manually, then no events are missed, but the review process becomes tedious and time-consuming
Solution Approach 1:
The system performs preliminary categorization and filtering of video segments before they reach manual review. By pre-analyzing segments and identifying those that contain meaningful motion events versus trivial movements, the system prepares the data in advance, allowing reviewers to focus only on segments that require attention rather than reviewing all detected motions equally.
Solution Approach 2:
The system automatically filters and categorizes motion segments, serving itself by identifying and eliminating false positives without human intervention. This self-service capability reduces the burden on manual reviewers by pre-processing the video stream and presenting only the most relevant segments for review, significantly reducing the time and effort required for complete event identification.
4Reliability
If continuous video data is stored and reviewed, then all events are captured, but the large amount of uneventful portions make review inefficient
Solution Approach 1:
The continuous video stream is divided into discrete segments based on motion detection events. Rather than storing and reviewing continuous video data, the system segments the video into meaningful units, storing and presenting only those segments that contain potential motion events. This segmentation dramatically reduces the volume of data requiring review while maintaining complete event capture.
Solution Approach 2:
The system extracts only the relevant portions of video data that contain motion events, separating them from the large amount of uneventful continuous video. By extracting and isolating segments with detected motion from the continuous stream, the system eliminates the need to review uneventful portions while ensuring all potential events are captured for review.
Data Source
AI summary
The various embodiments described herein include methods, devices, and systems for categorizing motion event candidates. In one aspect, a method includes receiving and processing video frames that include a motion event candidate. The processing includes: (a) obtaining background factors corresponding to a background in at least a subset of the video frames; (b) utilizing the background factors to identify one or more motion entities; (c) for each motion entity, obtaining one or more representative motion vectors based on a motion track of the respective motion entity; (d) identifying one or more features in at least a subset of the video frames; and (e) aggregating the background factors, the representative motion vectors, and the features to generate motion features. The method further includes sending the motion features to an event categorizer, where the event categorizer assigns a motion event category to the motion event candidate based on the received motion features.