Vehicle Camera Traffic-Signal Violation Detection with Selective Frames

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Solution Overview

Problem

Traditional vehicular safety and traffic compliance systems face challenges due to computational limitations in in-vehicle devices, leading to inaccurate and untimely traffic violation detections, which can result in safety hazards.

Innovation Solution

A vehicle camera system that processes video data using a forward-facing camera to identify danger zones, determines a point of no return, and selectively processes video frames to detect traffic signal violations, optimizing computational resources by minimizing continuous high-frame-rate processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If complex AI models are used to analyze live video feeds for traffic violation detection, then detection accuracy is improved, but power consumption and computational resource usage increase

Engineering Contradiction:

Improvetraffic violation detection accuracyVSAvoidpower consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system segments video processing into discrete frames and processes only specific frames (e.g., every nth frame or frames containing relevant objects) rather than continuously analyzing every frame at high computational cost, thereby reducing power consumption while maintaining detection accuracy

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs periodic processing of video frames at variable rates depending on driving conditions, using complex AI models only when necessary (e.g., when approaching intersections or detecting potential violations) rather than continuously, thus balancing accuracy with power consumption

Inventive Principle:

Principle #19Periodic action

2Loss of time

If complex AI models process every video frame at high frame rates, then detection timeliness is improved, but computational resource consumption increases

Engineering Contradiction:

Improvedetection timelinessVSAvoidcomputational resource efficiency

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The system dynamically adjusts the video frame processing rate and AI model complexity based on real-time driving conditions, such as vehicle speed, proximity to intersections, and detected object types, optimizing the balance between detection timeliness and computational resource efficiency

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary analysis on video frames using simplified models or heuristics before applying complex AI models, pre-identifying potential violations or critical moments that require full computational analysis, thereby reducing overall computational resource consumption while maintaining timeliness

Inventive Principle:

Principle #10Preliminary action

3Use of energy by moving object

If simplified AI models are used to reduce power consumption, then energy efficiency is improved, but detection accuracy deteriorates

Engineering Contradiction:

Improvepower consumptionVSAvoidtraffic violation detection accuracy

Core Design Contradiction:

Use of energy by moving objectVSMeasurement precision

Solution Approach 1:

The system applies different levels of processing quality to different video frames or regions of interest, using simplified models for background or low-risk areas while reserving complex AI models for critical regions (e.g., intersections, pedestrian zones, or areas with detected violations), thereby maintaining accuracy where needed while reducing overall power consumption

Inventive Principle:

Principle #3Local quality

4Reliability

If continuous high-frame-rate video processing is performed, then detection reliability is improved, but power consumption increases

Engineering Contradiction:

Improvedetection reliabilityVSAvoidpower consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system maintains continuous monitoring of driving conditions and video feed at low computational overhead, keeping the detection system ready and responsive without continuously deploying high-power AI models, thus preserving reliability while managing power consumption through selective activation of intensive processing

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20250336288A1Systems and methods for detecting traffic signal violations with reduced power consumption

Publication Date: 2025.10.30 VERIZON PATENT & LICENSING INC

AI summary

A device may receive data identifying danger zones for traffic signals associated with a vehicle, and may identify a set of danger zones for the vehicle. The device may retrieve a current location, direction, and speed of the vehicle based on determining that the vehicle has not reached a point of no return with respect to the set of danger zones. The device may identify a danger zone for the vehicle based on the current location, direction, and speed of the vehicle, and may process a video frame, with a model and based on determining that the vehicle has reached a point of no return with respect to the danger zone, to determine whether a traffic signal in the danger zone indicates proceed, stop, or yield. The device may perform one or more actions based on determining whether the traffic signal in the danger zone indicates proceed, stop, or yield.