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Computer Vision System for Traffic Management

Computer vision is transforming how we view and use roadway transportation systems. Vehicle sensors and cameras enable the collection of unprecedented volumes and types of data about local road conditions, traffic flow, drivers’ behaviours, safety risks.

Computer vision systems analyze this growing array of data streams, automatically identifying unusual or unexpected events that might pose safety risks or degrade overall system performance. Ultimately, the goal is to use this data for automated decision support, alerting road authorities in real-time to emerging problems so they address them.

Computer Vision

This blog gives an overview of current research and systems concerning Computer vision solutions for solving traffic management challenges like safety, efficiency, security, and law enforcement.

For Safety

Advanced Driver Assist System

It is an Intelligent Transportation System (ITS) designed for passengers travelling in automobiles and other vehicles. Automotive sensors, such as those on the front grill and side doors, are compatible.

In addition, vehicle apps such as video technology and computer vision are also available. They sense the environment to extract data and aid the automobile in travelling safely, demonstrating the best progress in driver assistance and road safety.

Intelligent Lane Assist

When cars change lanes on roads, sadly, many accidents occur. Roadway alarm systems prevent this only on a few roadways. Modern vehicles are equipped with the LDW (lane departure warning) system to make our ride safer. The Computer vision aid the Lane Departure Warning to provide an alert to the driver if they are beginning to leave the travelling lane

Intelligent Lane Assist

It uses a camera positioned on the side view mirror or in the dashboard to follow lane markers on the road and to view the car’s surroundings.

Unfortunately, if a vehicle crosses a lane without the driver present, an alarm is issued to the driver. Such as a sound or seat vibration. When an alert is triggered, the car CPU calculates the speed of the automobile. And the angle of the road to determine the accuracy of the LDW.

LCA (Lane Change Assist), referred by ADAS, is a new upgrade in lane assist that audits adjacent lanes when a vehicle is present and alerts the driver if a lane change starts and the side lane is engaged. Computer vision assists LDW and LCA in upgrading their features daily, but more research and development are required.

Smart Pedestrian Detection

Pedestrians are injured and die every year while crossing roads. To avoid this, we have Pedestrian Detection. It’s a rapidly growing field of computer vision that aims to detect people crossing highways in real-time.

Although existing algorithms effectively detect pedestrians, they require a significant amount of manual data labelling for training. It uses video Surveillance from cameras mounted on moving vehicles and detects car and pedestrian distance, shape, texture, appearances, varying pose, clothes, and illumination changes in many lighting conditions.

It also uses longitudinal motion and changing image backgrounds. Such as rainy, winter, and summer, detect motion in pedestrian images from frame to frame. When combined with bounding box dimensions, edges, and sizes, cameras can also provide disparity data.

We can achieve Pedestrian detection with computer vision by simply scanning an image in several dimensions, using an object detection module, and combining this with non-maximal suppression to find objects in images. Computer vision improves accuracy in detecting pedestrians.

Driver Monitoring

It’s a video processing and computer vision system that monitors drivers’ focus while driving and alerts them if they’re driving dangerously. It is Processed with a camera located inside the car and a driver’s mobile camera. So road conditions, driver’s face, and cameras are all being monitored.

Drowsiness, attention, and gaze are all detected using a camera that catches the driver’s face. With a Binary support vector machine classifier, drowsiness counts how many times a driver closes and opens his eyes.

According to reports, with 425 closed-eye shots and 1355 open-eye shots, this detector has a 93 percent accuracy rate. It can also identify drowsiness in various lighting conditions and while the driver is wearing sunglasses. In addition, numerous sets of videos are recorded and coupled with microsleep modes to comprehend eye states.

In comparing cameras with Biosensors, they measure photoplethysmographic, electrocardiographic, and electroencephalographic data for detecting drowsiness in drivers. In addition, these sensors outperform cameras in terms of data bandwidth and processing. As a result, cars may be equipped with biosensors in the future to detect driver tiredness.

New Adaptive and Warning Systems

High Beam / Low Beam Headlight Control systems

It’s a great way to spot a car 400-500 meters away. To achieve this, Video processing and camera-based sensing are primary.

The host vehicle’s tail lights can be detected at a distance of 400 to 500 meters when the high beams of the headlights are off, and oncoming cars see at a distance of 800 to 900 meters when the high beams of the headlights are off.

Traffic Sign Recognition

It’s an advanced AI Recognition technique in which computer vision cameras can notify the driver if he joins a one-way traffic route, passes through car-free roads, stops at stop signs, and drives around road curves and intersections.

These notifications, when combined with pedestrian detection, resulting in a less stressful journey. It also offers various other detecting models, such as colour and shape.

For example, in colour, the model detects colour on traffic signs and integrates data with machine learning models for segmentation in the colour model. For practical road sign segmentation, neural networks and SVM classifiers combine to improve performance.

It can detect road signs in polygons using a shape model, while another approach uses a Harris corner detector and trace corners with predefined planning and design process to detect rectangular and triangular shapes.

The SVM classifier is also employed to determine round shapes, and the Viola-Jones detector uses 899 warning sign labelled images and 1000 random negative sample images. All of these techniques aid in the recognition of traffic signs.

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Adaptive Cruise Control

Imagine a car that slows down or comes to a complete halt when a vehicle passes out front. Adaptive cruise control calculates the distance between your vehicle and the car next to you and adjusts your speed to avoid a collision. This feature enhances driver safety, comfort, and enjoyment. It functions as a backup driver, with stereo imaging, laser radar, and millimetre-wave radars as a source.

For Efficiency

Governments can use traffic data to plan road improvements, schedule maintenance, and calculate traffic periods. This information helps with incident detection, verification, and reaction, as well as effective incident management.

Traffic Flow

Traditional traffic forecasting models are unreliable and costly. Computer vision technologies, based on Artificial Intelligence, can detect real-time traffic by recording video sequences. In addition, it can integrate with current surveillance cameras for tolling and law enforcement as a booster.

Today Computer Vision Traffic management systems can measure

  • Vehicle speed and traffic flow
  • Vehicle length
  • Inter-vehicle gaps
  • Multilane vehicle counting
  • Tracking critical traffic situations
  • Traffic queues, and
  • Traffic stoplights monitored by AI traffic management systems

Some advanced systems record a virtual representation of road conditions and classify vehicles using background subtraction, edge detection, and shadow rejection and using geometric car models to locate a vehicle’s exact location.

A backpropagation neural network and machine learning algorithms are employed to determine Vehicle Queue lengths at traffic intersections.

For cost-effectiveness, specific road cameras decreased, resulting in a process known as vehicle Reidentification, in which the camera detects a vehicle that matches, leaving one monitored region with the exact dimensions, look, and colour. All of these computer vision techniques aid in the counting of vehicles and the analysis of traffic patterns.

Incident Management

On the road, an incident happens when free traffic movement is slowed or halted owing to traffic lane closures or other restrictions on free-flow traffic. Primary instances include chemical spills, road debris, accidents, and stranded cars.

Road accidents are two categories: primary and secondary. Secondary incidents emerge as a result of significant incidents. According to traffic statistics, secondary occurrences account for 50% of all incidents. It acts as a spotlight for substantial concerns to resolve.

Computer vision cameras detect slow-moving traffic and halted vehicles to prevent primary and secondary mishaps. Glare, rain, snow, and shadows are the most likely reasons for incidents. Robust incident detection systems track vehicle movements, causing collisions, and present an overview for accident investigators.

It was tested to its limitations in various traffic circumstances, including severe fog, snow, rain, and excessive traffic.

With video-based speed estimate and sensor geometry parameters, data acquired from all traffic intersections and roads is applied in non-overlapped areas to anticipate vehicle travel time in a blind spot in two disjoint coverage areas.

Traffic congestion, stranded automobiles, and vehicles travelling in the opposite direction were all detected by this technology.

Automated Tolling

Open road tolling, often known as ORT, began in 1959 and allowed to collect money without physical toll booths. Advanced client experience, congestion management, network operations, and optimal pricing benefit this tolling system.

Computer Vision

It’s a video tolling system that charges tolls based on vehicle classification. This tolling system detects license plates and takes high-speed images with RFID devices (transponders), which entails the installation of tags on vehicles—first initiated in North America.

The video module calculates departure and entry points, license plate identification, and driver data to generate toll bills based on distance travelled and vehicle category.

License plates with a valid transponder in other systems are collected, and advanced techniques correlate license plates with customer data to toll fees paid upfront. In addition, some places collect toll fees on a daily, monthly, or annual basis, depending on their preferences.

For Law Enforcement and Security

Computer Vision Solutions play a vital role in defining law enforcement and security solutions as they offer features that meet security and law enforcement regulations. In other cases, both are distinct. For example, security apps prioritize prevention and prediction, but law enforcement prioritizes proof and accuracy.

In many cases, security served as the first line of defence for law enforcement. For example, we are Identifying and recognizing potential explanations of infractions. For a better understanding, we’ve presented a few samples below.


Security cameras are vital for surveillance and security, and they are inexpensive simple to install and operate. They provide operators with immediate and dynamic visual data, enabling integrated operations and allowing operators to work in many locations. In addition, it’s simple to look at any video that entices our curiosity.

Video recording and sensing powered by computer vision technology provide numerous advantages. Assume that the output of these cameras is examined and processed by manual operators and that backups are made for future usage to track down any events.

The traditional one is more expensive and prone to errors, but it lacks prevention and prediction features. On the other hand, computer vision and video analytics extraction is a dynamic domain to take full advantage of video sensing.

Warning and Alert Systems

Quick video analysis can save lives in some instances, and an amber alert is an emergency alert system that notifies individuals when their loved ones or children go missing. It was a massive hit in several countries and gained worldwide acclaim. Suppose we have any evidence about the identity, such as the license plate number colour.

It’s simple to scan video databases for clues gathered from local roads, traffic intersections, stop sign monitoring, and other sources. Like an amber warning, a silver alert is a notification issued by local authorities when a mentally ill or older adult is missing.

When detecting a suspect car with monitoring and video recording devices, consider them as eyes that help law enforcement save a child’s life. In large video datasets, new technologies have developed dynamic searching models for cars.

System Using Adaptive compression and suitable decompression techniques in advance. Rather than selecting reference compression frames in defined time intervals as is customary. Depicted Reference frames are places where vehicles are in the best feasible viewing position. Decompressing reference frames allows for hours of car video to view.

When selecting a reference frame on specific films, algorithms will decrease the search space connected to methods. Depending on traffic solutions, the size of the search space lowered. Therefore, it is suitable for medium to low traffic volumes.

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Switching is a complicated process in which reference frames inserted at predefined rates provide the most significant boost to a simple approach introduced at predetermined rates. Ongoing with a specific application of complex video retrieval and search. In some cases, computer vision is helpful.

Traffic Surveillance

Computer vision in traffic surveillance addresses human behaviour prediction, abnormal incident detection, illegal turns, and aggressive driving. Low-level, mid-level, and high-level computer vision solutions are all available for traffic surveillance.

Low Level

Tracking and object detection is the basis of any surveillance system. However, compared to other levels, the data required for low-level computer vision tasks are minor. Pixel intensity, which fluctuates over time intervals from frame to frame, is used to detect an object.

To verify in the background, the statistics of local pixel intensity seem like a gaussian mixture model. Motion analysis employs optical flow or motion vectors, on the other hand. It is leveraged directly for locating particular objects such as vehicles via machine learning and pattern recognition algorithms. However, computation is more expensive.

We have several direct methods available when an object is detected, including Template matching, mean shift, and others—trajectories for identifying and tracking objects used to track.

In addition, we may count the number of vehicles, the pace of traffic, and more in this series. Anomaly detection, road incident detection, illegal turn detection, and access control are more advanced tracking features.


We’ll need a mid-level to accomplish this. Therefore we’ll look at the patterns or dynamics of these trajectories. The classification of mastering patterns or trajectory dynamics is extensive. This challenge is suitable for all machine learning approaches. In the training phase, a common goal is to accomplish clustering based on related dimensions. The generated clusters demonstrate the behaviour of each group at the maximum point.

When new trajectories founded in any traffic scene, they are compared to these models to identify events of interest, such as anomalies and incidents. They can also detect group behaviours. We can’t just use machine learning techniques; we need a broad understanding of current traffic circumstances.

In the presence of surveillance cameras, for example, not all vehicles can move in the same direction at the same pace. It differs amid different trajectory models. To do so, we’ll need to take a broad step to estimate travel distance.

In another case, when the same pattern is used as a symbol indicating traffic congestion on a highway segment, the stop and go trajectory approach is acceptable. I believe that now you have a better understanding of what trajectory analysis entails and how many ways are required to achieve it at the intermediate and upper levels of the surveillance hierarchy.

For example, In Video Surveillance, self-organizing neural networks, non-deterministic finite automation, syntactic approaches, time-delay neural networks, hidden Markov models, finite state machines, dynamic time warping, and other technologies are used.

All of these techniques use to master behaviours and complete a high-level visual task. Computer vision applications for urban traffic surveillance and highway traffic surveillance were reviewed recently in a survey report.

In terms of road usage and scenarios, urban traffic surveillance is more complex than highway traffic surveillance. When comparing a highway scenario to an urban environment, vehicle trajectories exhibit simpler patterns and pedestrian detections. Vehicle-pedestrian interactions aren’t critical, and occlusion isn’t complex, according to the report.

In the early stages of development, systems followed a simple framework in which object tracking was without object classes. As computer vision evolves, a mastery of object classes must keep track and solve progressively tricky tasks.

The new Framework is more precise in terms of performance, but it comes at the cost of extra processing time. In addition, because these frameworks are application-specific, they require more in-depth evaluation in natural environments.

High Level

Anomaly incident detection explains using a high-level computer vision model. Unattended baggage on public transit, risky pedestrian driver behaviours, accidents, traffic violations is just a few examples. There are two types of detections: supervised and unsupervised. Anomaly detection reduces classification concerns in a supervised model.

Outlier detection is a problem with unsupervised models. Many anomaly detection systems use the improvements above in object tracking in the transportation industry to classify normal and anomalous situations in addition to road trajectories.

Sparse reconstruction is a new technique for detecting anomalous vehicle trajectories. In the initial training phase, regular trajectory classes are taught and labelled manually with a semantic category or acquired using an automated unsupervised model.

The assumption is that the most recent typical trajectory locate between a linear span of specific trajectories of the same class and maybe rebuilt by incorporating a short dictionary entry. A reconstructed coefficient vector is also sparse. A sparse reconstruction vector is not achievable when reconstructing anomalous trajectories.

Since they comprise many dictionary elements from multiple classes, anomaly detection is reduced to a sparse reconstruction of the test trajectory, with the leading dictionary serving as the training dictionary, followed by sparsity measurement.

Another model of a single object event uses an extensive sparsity framework to combine the modelling of many object events. Both models perform sparse reconstruction with L1 reduction, and it initiates kernel to class distinction. In short, a variety of computer vision solutions employ for human and traffic surveillance.

This decade has made many breakthroughs at the low, intermediate, and high levels, and further understanding of computer vision tasks as prediction and behaviours in instances is essential. Like occlusion, shadows, clutter, varying illumination, and noise abound. In addition, we see more advanced systems with dynamic technologies that incorporate data from various traffic intersection cameras acquiring more attention in the future.

Vehicles of Interest

Our concern’s tracking and identification vehicles necessitate dynamic computer solutions in recognition, classification, and detection domains.

Vehicle classification and recognition require a variety of granularities for various applications, ranging from classifying vehicles from small to large, in detail SUV, sedan, and other car types, to Unique Vehicle Identification, which involves identifying a car using the alphanumeric data of a license plate.

The Dynamic Technology used for vehicle classification design to employ light curtains, which provide a 3D image of the vehicle with sensors and line brightness perpendicular to traffic flow. Because of their multipurpose

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capabilities and the growing popularity of roadside cameras, recent vision technologies use in various sites. Such models have attained different levels of class granularity, which are dependent on the application conditions.

A model, for example, employs a 3D vehicle model that is compatible with a wide range of vehicles. This model’s elements refine using the best 3D model, which matches and predicts picture intensity to produce images. Verifying a fitted model element yields the vehicle class.

Many trials carried on five-class issues such as pickup trucks, SUVs, minivans, and four-door sedans and fundamental three-class topics such as pickup trucks, minivans/SUVs, and sedans. This method’s performance is exceptional.

Other models used supervised learning rather than 3D models. It’s a virtual loop method that uses video analysis and virtual loop motion vectors to adjust the behaviour of precise inductive loop detectors. The primary vehicle class is the same as ILD (Inductive loop detectors), a vehicle length-based model of reviewing at a single-dimensional symbol of ILD output.

When compared to a deformable template model, classification capabilities are limited. Aerial videos classify cars, pickup trucks, vans, and SUVs based on their size, shape, and look.

Modified SIFT descriptors and edge points use as inputs for vehicle classifiers where the developers of these models show impressive performance leveraging supervised machine learning to detect sedans and SUVs.

For Law Enforcement

The Overview of Violations in Law Enforcement Apps are unconditionally available for system developers. It is especially true with security software when the incident is not apparent and requires special attention. This paradigm simplifies the problem in other sections, but identification, certainty, and accuracy are analytical. For Instance, Speed enforcement necessitates greater precision and insight.

Speed Enforcement

Many studies have found a direct interaction between traffic accidents and increased vehicle speed, and speed enforcement has demonstrated a significant improvement in reducing vehicle speed, with photo enforcement of speed reduction leading to a decrease of 21% to 14% in accidents.

Calculating vehicle speed is vital in detecting events, predicting accidents, and optimizing traffic flow. Video cameras, lidar, radar, and inductive loops use to achieve vehicle speed in traffic intersections.

Enforcement at Road Intersections

based law Enforcement at road intersections can detect blocking the box incidents and prohibited turns. Detecting red light violations and their link to accidents is most popular.

Red light camera systems work on the basis that a camera work by an event recorded by a reasoning algorithm and has access to signals, like those from traffic light systems and ILD on a stop line. Since camera work relies on vehicle identification and evidence retrieval, computer vision systems rely heavily on LPR.

Another model, for instance, is based on full vision and traffic lights that track and detected automatically using video and image processing algorithms and vehicle recognition at traffic intersections to achieve red light violations. It eliminates the need for ILD and interaction that is present in most other models.

Anomaly detection, vehicle trajectory analysis, vehicle tracking, vehicle detection, and other computer vision technologies are examples. In addition, more law enforcement apps are expected for detecting illegal turns, blocking the box events, and jaywalking.

Mobile Enforcement

Another set of new applications includes cameras mounted on school buses and parking enforcement vehicles and focus cameras mounted on fixed locations such as utility poles. Various computer vision approaches are required regardless of the type of application.

Many models were there to fix camera models and used all these models. Camera mobility has advantages such as flexibility and low-cost coverage areas and introduces additional problems as unknown camera motion patterns.

Furthermore, a mounted camera on a vehicle can impose various constraints and limitations on the camera field of view compared to a fixed camera setup.

For example, a camera installed on the top of a cop car is lower than a camera mounted on a pole, resulting in more challenges to resolve. This camera’s job is to detect vehicles having LPR technology for owner identification.

Another app is parking enforcement, which uses LPR technology for unique car identification and image processing technologies for vehicle signature matching to track parking spaces.

Signature matching can be used with license plate info to obtain vehicle-specific geolocation, human verification, and time. As in the future, computer vision solutions such as object/motion detection will automate the entire workflow.

In short, several computer vision systems can compute an individual vehicle’s speed as a direct output. However, single-camera systems have an accuracy issue, whereas stereo cameras for photo enforcement are readily available. In addition, there are limited studies on the calibration and accuracy of 3D designs. Another common 3D system technique integrates lidar/radar for speed and a camera for vehicle detection and evidence archiving.


When it comes to adopting computer vision technology to solve transportation problems, we face two significant challenges. First, many algorithms are now available, offering better performance in specific limited settings but are ineffective in real-time scenarios.

The main challenge is to create algorithms that provide higher accuracy and reliability for various characteristics such as traffic behaviour, capturing geometry, illumination, and weather.

The second problem is cost-effectively implementing this algorithm on adequate infrastructure. Within vehicles and infrastructure, image, computer vision, and visual intelligence all required a degree of attention to minimizing current tech costs.

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I'm Suresh, a Sr. Technical Content Writer at Visionify. I bring my experience to my current role to write technical content, blog articles, etc. on Artificial Intelligence, Machine Learning, and Computer Vision technologies.
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