Camera-Based Delivery Event Detection Through Movement Patterns
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Solution Overview
Problem
Existing delivery notification systems often fail to accurately detect delivery events due to issues such as undetectable items, poor lighting, or items being outside the camera's field of view, leading to inaccurate user notifications.
Innovation Solution
A system that uses camera-based video processing to recognize movement patterns and contextual information, such as the behavior of delivery personnel, to determine delivery events, even if the item itself is not visible in the video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies on detecting delivered items in the video, then the detection accuracy may be high when items are visible, but the system fails when items are too small, in poor lighting, or outside the camera's field of view
Solution Approach 1:
The system introduces movement pattern analysis as an intermediary method to detect delivery events. Instead of directly detecting the delivered item, the system analyzes the movement patterns of people and objects in the video. When item detection fails (due to size, lighting, or field of view constraints), the movement pattern analysis serves as a complementary detection mechanism that identifies delivery events through behavioral cues such as approaching the door,停留ing for a specific duration, and leaving with a package
Solution Approach 2:
The system implements a multi-functional detection approach that combines multiple detection methods: direct item detection, movement pattern analysis, and contextual information processing. This universal system can handle various delivery scenarios regardless of item characteristics, lighting conditions, or camera positioning by switching between or combining different detection functions
2Device complexity
If the system uses only item detection methods, then the system is simple, but the detection accuracy decreases when items are not visible
Solution Approach 1:
The system segments the delivery detection task into multiple independent analysis components: item detection module, movement pattern analysis module, and contextual information processing module. Each segment handles specific aspects of delivery detection, allowing the system to maintain reasonable complexity while improving overall accuracy through the combination of specialized sub-systems
3Measurement precision
If the system analyzes movement patterns and contextual information, then the detection accuracy improves, but the processing complexity increases
Solution Approach 1:
The system applies partial action by selectively analyzing only relevant portions of the video data. Instead of processing every frame in detail, the system focuses on detecting specific movement patterns and contextual cues that indicate delivery events, reducing unnecessary processing while maintaining detection accuracy
Data Source
AI summary
Systems, apparatuses, and methods are described for detection of an item being delivered at a premises. Based on a movement pattern shown in a video captured by a camera associated with a premises, a delivery event may be determined. A notification of the delivery event may be sent to a user device associated with the premises.