Camera Human Activity Detection Using Co-Occurring Motion Cues

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Solution Overview

Problem

Surveillance cameras often struggle to accurately detect human activity due to factors such as distance, occlusion, or insufficient illumination, leading to inefficiencies in power consumption and delayed notifications.

Innovation Solution

The system employs a camera with a human detector and optical flow analyzer to identify human activity through co-occurring motion patterns, allowing for the detection of humans based on the analysis of grid segments in video frames, thereby reducing the need for continuous human detector processing and conserving power.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the human detector processes all video frames continuously, then human detection accuracy is maintained, but power consumption increases

Engineering Contradiction:

Improvehuman detection accuracyVSAvoidpower consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary analysis using optical flow detection to identify frames containing motion before applying the computationally intensive human detector. This preliminary filtering action allows the human detector to be skipped for frames without human activity, significantly reducing power consumption while maintaining detection accuracy when humans are present

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Instead of applying the human detector to all frames (excessive action), the system applies it only to frames where optical flow indicates potential human activity (partial action). This selective application reduces energy consumption while ensuring human detection accuracy is maintained for relevant frames

Inventive Principle:

Principle #16Partial or excessive action

2Adaptability or versatility

If the camera detects human activity at greater distances or through occlusion, then detection coverage is improved, but detection accuracy decreases

Engineering Contradiction:

Improvedetection coverageVSAvoiddetection accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system uses optical flow analysis as an intermediary to detect motion patterns in regions where humans may be present but not clearly visible. By analyzing co-occurring motion of scene entities (such as door movement, vehicle movement, or foliage movement) that typically accompany human activity, the system extends detection coverage to distant or occluded humans while maintaining accuracy through pattern recognition

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces direct visual detection of humans (mechanical/optical detection) with detection of co-occurring motion patterns. This substitution allows the camera to detect human activity indirectly through the motion of other objects that accompany human presence, thereby extending detection coverage without sacrificing accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If the system sends notifications for all detected motion events, then notification completeness is improved, but false alarms increase

Engineering Contradiction:

Improvenotification completenessVSAvoidfalse alarm rate

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system uses feedback from optical flow analysis to inform the human detector which frames warrant notification. By analyzing motion patterns and determining whether they correspond to human activity based on co-occurring motion patterns, the system provides feedback that filters out non-human motion events, reducing false alarms while maintaining notification completeness for actual human activity

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12579871B2Camera detection of object movement with co-occurrence

Publication Date: 2026.03.17 OBJECTVIDEO LABS LLC

AI summary

Methods, systems, and apparatus for camera detection of human activity with co-occurrence are disclosed. A method includes detecting a person in an image captured by a camera; in response to detecting the person in the image, determining optical flow in portions of a first set of images; determining that particular portions of the first set of images satisfy optical flow criteria; in response to determining that the particular portions of the first set of images satisfy optical flow criteria, classifying the particular portions of the first set of images as indicative of human activity; receiving a second set of images captured by the camera after the first set of images; and determining that the second set of images likely shows human activity based on analyzing portions of the second set of images that correspond to the particular portions of the first set of images classified as indicative of human activity.