Addressing Intersection Congestion through Video Object Detection

Based on Patent Research | CN-112784789-A (2024)

Road intersections require precise monitoring to manage vehicle flow effectively. Current systems often fail to measure lane queuing accurately, causing congestion and wasted energy. Video object detection solves this by using image sequences to identify and track individual vehicles across multiple frames. This automated method provides continuous data for traffic controllers and autonomous systems. These tools help reduce wait times, improve lane efficiency, and support scalable electronic infrastructure for smarter urban travel.

SOLUTION OVERVIEW

From Manual Monitoring to Smart Detection

Video object detection technology provides a sophisticated response to traffic management hurdles for the computer and electronic products sector. This system gathers visual data from cameras and processes image sequences to identify and track moving vehicles. By analyzing consecutive frames, the software calculates the precise location and movement patterns of each car. This continuous sensing creates a real-time data stream that informs traffic controllers about lane density and queuing behavior, turning raw visual input into actionable traffic intelligence.

The potential for automation allows this technology to integrate directly with smart traffic lights and electronic controllers. By replacing manual observations with high-speed algorithmic processing, urban infrastructure becomes significantly more responsive. This is much like how a modern motherboard manages data traffic between components to prevent bottlenecks, ensuring the entire system runs smoothly. Implementing these advanced sensors leads to optimized signal timing and more efficient energy use. These innovations foster a more reliable and intelligent urban transportation landscape for future growth.

HOW IT WORKS

Mining Traffic Flow from Video

Capturing continuous visual input streams

Optical sensors installed at road intersections record high-resolution video of traffic flow and lane occupancy. These cameras transmit sequences of images to a central processing unit where the visual data is digitized for analysis. This step ensures that every vehicle entering the intersection is documented as a clear data point.

Identifying vehicles through image processing

The system scans each frame to locate and classify individual objects like cars, trucks, and motorcycles. By analyzing pixel patterns, the software distinguishes vehicles from the road surface and background environment. This process transforms raw imagery into identified hardware entities within a digital coordinate system.

Tracking vehicle motion across frames

Computer vision algorithms link detected objects across consecutive images to monitor their speed and direction. By measuring how a vehicle moves from one frame to the next, the system calculates precise queuing lengths and wait times. This temporal analysis allows the electronic infrastructure to understand the dynamic behavior of traffic over time.

Converting visual data into commands

The final processed information is sent as a real-time data stream to smart controllers and electronic signal systems. These devices use the traffic intelligence to adjust light timings and optimize vehicle throughput across the network. This automated output reduces congestion and improves the overall efficiency of urban electronic infrastructure.

Potential Benefits

Enhanced Real Time Traffic Intelligence

Video object detection converts raw visual data into immediate actionable insights regarding vehicle density and movement patterns. This allows electronic control systems to respond dynamically to changing road conditions instead of relying on static schedules.

Improved Urban Infrastructure Efficiency

By automating the tracking of vehicle queues, the system optimizes signal timing to reduce congestion and idle times. This leads to significantly lower energy consumption and better hardware performance across smart city networks.

Seamless Hardware Integration Capabilities

The technology integrates directly with modern electronic controllers and sensors used in smart urban travel. High speed algorithmic processing ensures that traffic data flows as smoothly as data between components on a motherboard.

Scalable and Reliable Monitoring

Automated computer vision replaces manual observation with consistent and continuous sensing across multiple frames. This scalable approach provides highly accurate data for autonomous driving systems and future urban infrastructure growth.

Implementation

1 Install Optical Sensors. Mount high-resolution cameras at intersections to capture clear visual streams of vehicle movement and lane occupancy.

2 Configure Processing Units. Establish a central computing environment to receive and digitize video data for real-time image analysis.

3 Deploy Detection Models. Integrate video object detection algorithms to identify specific vehicle types and track their movement across frames.

4 Establish Controller Integration. Connect the AI output stream to electronic traffic signal controllers to automate signal timing based on density.

5 Optimize Network Throughput. Calibrate the system parameters to refine queuing calculations and maximize the efficiency of urban traffic flow.

Source: Analysis based on Patent CN-112784789-A "Method, apparatus, electronic device, and medium for recognizing traffic flow of road" (Filed: August 2024).