Video Object Cropping via Motion Tracking and Quality Scoring
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Solution Overview
Problem
Existing video surveillance systems lack interactivity, fail to generate alerts or alarms based on object detection, and do not typically produce cropped images based on object classification, motion, or visibility, and are not computationally influenced by the transitory nature of detected objects.
Innovation Solution
A system and method that utilize a processor to detect objects in video streams, calculate motion, generate cropped images (hyperzoom images) based on confirmed motion tracks, and transmit these images to remote devices with quality scores, allowing for user-defined alerts and alarms, and incorporating user inputs for enhanced object tracking and image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cropped images are generated for every detected object in video surveillance, then image quality and object detail are improved, but data transmission load and processing complexity increase
Solution Approach 1:
The system changes parameters by generating cropped images selectively based on motion detection and classification confidence thresholds rather than for all detected objects. This parameter-based filtering approach maintains image quality for relevant objects while reducing overall processing complexity and data transmission load.
Solution Approach 2:
The system applies partial action by generating cropped images only for objects that meet specific criteria (motion detection, classification confidence) rather than for all detected objects. This selective approach provides sufficient image quality for important objects without the excessive processing burden of generating crops for every detection.
2Reliability
If all detected objects are tracked and monitored, then object detection completeness is improved, but false alarms and computational load increase
Solution Approach 1:
The system performs preliminary actions by detecting motion and generating classification predictions before generating cropped images or triggering alerts. This preliminary filtering based on motion detection and classification confidence reduces false alarms while maintaining completeness for truly relevant objects.
Solution Approach 2:
The system uses feedback mechanisms where classification confidence scores and motion detection results inform subsequent decisions about whether to generate cropped images and trigger alerts. This feedback loop allows the system to maintain detection completeness while filtering out low-confidence detections that would generate false alarms.
Data Source
AI summary
A method for generating cropped images depicting objects captured in video data includes receiving, at a processor of a video camera system, a video stream including a series of video frames depicting an object. A classification for the object is generated, and an occurrence of the object being detected is identified in an additional video frame(s) from the series of video frames. A motion associated with the object is calculated based on the classification and the additional video frame(s). At least one image that depicts the object and includes a cropped portion of a video frame from the series of video frames is generated, along with an associated set of at least one quality score, in response to calculating the motion. The method also includes causing transmission of the at least one image to at least one remote compute device based on the set of at least one quality score.