Vehicle detection and counting systems play an essential role in an intelligent transportation system, especially in the traffic management process. This vision-based system will monitor roads, then detect vehicle type and count those vehicles.
Vehicle detection aims to contribute information related to
Cities and traffic congestion have grown shoulder to shoulder since the earliest large human settlements. Traffic congestion has remained one of the most stubborn problems facing road users and urban executives across the globe. Global Traffic Volume (GTV) is determined to double between 1990 and 2020 and again by 2050. This amount of growth indicates what the future of traffic congestion would mean for people living and working in urban areas. Everywhere in the world, in both large and small cities, traffic congestion is becoming worse. This traffic congestion is the elevated cost of living and loss of time for other activities.
The most developed and developing cities currently implement video surveillance systems, which results in vast amounts of video data stored in servers. In a particular town, most CCTV cameras capture traffic scenes on urban roads. Furthermore, command centers and traffic management offices require this knowledge to respond to crises and get visual traffic conditions. CCTV operatives monitor these live-feed data to record and report any incidents that have happened 24/7. With this, little or no time is given for video analysis, such as counting vehicles, which is crucial information in planning and making decisions to better urban traffic flow and willingness to emergency events.
In many cities around the world, traffic problems are an essential concern. The traffic problem has many prominent causes. Due to more qualified health care, better education, better job possibilities, and well-built houses, people migrate from rural to urban areas. And the number of citizens transferring to metropolitan cities has increased dramatically, resulting in a drastic rise in the number of vehicles. Due to the increasing number of residents, the roadway’s capacity has grown inadequate and relatively slow. In large cities, this causes roads and the number of vehicles to be imbalanced, resulting in road congestion.
The real-time and effective vehicle counting method is significant to implement traffic management and control violations as a core component of intelligent transportation. However, the vehicle detection and counting system will give information related to
The ai-based vehicle detection and counting system play an essential role in the transportation systems to get smarter cities. It is computationally effective and reliable to classify the type of vehicle and count them in real-time using the videos of CCTV cameras.
Vehicle detection and counting system is a key strategy of traffic analytics that can be used on highway and urban roads under different weather and traffic conditions. Moreover, it can be easily integrated with already existing or newly installed CCTV cameras.
Critical features of vehicle detection and counting system
Few Vehicle Detection & Counting Applications :
Surveillance cameras in roads have been broadly installed worldwide, but traffic pictures are rarely openly released due to copyright, privacy, and security concerns. Therefore, we collected those images from various sources, such as those taken by the car camera and the surveillance camera and those taken by non-monitoring cameras of multiple lighting conditions and different weather conditions.
Our annotator team started labeling the vehicles by drawing bounding boxes around vehicles such as cars, trucks, bikes, etc. Then the verification team verified the annotated images whether vehicles in the images were tagged correctly or not; if not, then again those images are assigned to the annotation team.
In the real world, images are full of noise. Because of camera quality, weather conditions, we can’t take pictures without noise most of the time. Therefore, before developing an AI model, we should eliminate that noise from the image. Otherwise, the model will learn the wrong patterns from noise images.
We are applying noise removal techniques to get denoised images. We also used edge detection methods to extract the shape of the vehicle.
AI-based vehicle detection and counting systems are almost new compared to their non-video-based counterparts. This reason is mainly due to improvements in image processing and systems infrastructure over the past two decades. As a result, there’s a lot of research on techniques and algorithms for video-based vehicle detection and counting. These methods majorly consist of detection, tracking, and counting techniques. For an AI-based system to work, it has first to identify vehicles driving on the road. Next, it has to track their journey until they leave the frame of the video capturing stream. This method is essential to avoid double counting vehicles. The last step is counting, which has to occur before a vehicle leaves the video frame.
Object detection is a computer vision and image processing technique that identifies objects of a specific class, like vehicles or people, in videos. Object detection can solve complex real-world problems in different areas like image search and video surveillance. In addition, it is utilized widely in computer vision tasks, including face detection, face recognition, and object tracking.
The vehicle detector identifies vehicles in a given frame or image. It can detect various vehicle types, including cars, buses, trucks, motorbikes, and bicycles, and delivers a list of bounding boxes for all identified vehicles. The bounding box consists of the x-y coordinate of a vehicle as well as its width and height.
Tracking is the process of following the path or movements of an object to find it or observe its course using a camera. The uses of video tracking include surveillance, security, and traffic control, etc.
The vehicle detection and counting system tracker receives a set of bounding boxes that identifies regions of interest (ROI) to track. Then, it generates blobs from each bounding box and begins following them across the video frame.
Counting is the final step which includes determining the number of vehicles that have moved at any given period. Vehicle counts may be noted on the counting device or forwarded to a remote location over the internet.
The AI-based vehicle counter observes blobs to see which ones have crossed a proposed “counting” line. The counting line is formed at a close to where the vehicles exit the video frame. Once a blob crosses the counting line, it is counted, and its count status is updated so that the system does not double matter it.
The improved vision-based or AI-based vehicle detection and counting system were employed to warn congestion and queues at work zones and on freeways during special events. The proposal traffic warning system consists of a set of video monitoring stations provided with video recording devices and an AI-based vehicle detection system. As a result, vehicle queue lengths, speed, and counts are observed before work zones or special event places and real-time information regarding congestion.
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