Emotyx is an AI-powered real-time video analytics software that enables businesses to harness intelligent insights from CCTV videos. It automates security surveillance, customer tracking, crowd analytics, people counting, vehicle counting, facilities tracking, heatmap analytics and many more. With Emotyx video analytics suite, you can convert your ‘passive’ CCTV infrastructure into an active business efficiency analyst.
With the growing vehicle population, the demand for artificially intelligent solutions that can automate vehicle counting and vehicle classification is also increasing. World Economic Forum estimates that the total number of cars on road would reach two billion by 2040 and 790 million trucks by the same time. Even though governments are actively constructing new bypasses and roads to accommodate these projections, the rise in traffic complexity would greatly impact the traffic congestions across road networks in a metropolitan area. This has encouraged authorities and businesses to look for more efficient ways to manage traffic and route vehicles in a streamlined manner.
The ability to acquire data on the number of vehicles in a particular area can be extremely useful to businesses that deal with traffic management, logistics, ports, construction, etc. Most of these companies still use the traditional method of manual counting and classification of vehicles. But, the process can be highly inefficient, prone to errors, and above all, wastage of valuable manpower in doing monotonous jobs. The most promising solution for this pressing issue would be Artificial Intelligence. An AI model can be easily be programmed to count the number of vehicles in a particular area to monitor the influx. This article will help you gain better insight into AI-powered real-time vehicle counting and classification using AI.
Traditionally, organizations employ humans to perform tasks like vehicle counting and classification. These processes involve manual traffic condition monitoring and vehicle identification. Generally, a person will be made to stand at a point and note the count of the vehicles and their types. But, this can be a very difficult and time-consuming process. Also, human errors and inaccurate judgments are bound to happen in such cases. This can lead to errors in reporting. To tackle this issue, a few businesses have introduced sensors into their business operations. But unfortunately, this can only solve the issue of vehicle counting. The type of vehicle cannot be recognized by sensors, which is extremely inconvenient.
Traffic Management Centres (TMCs) primarily make use of vision-based cameras to monitor traffic activities. Most of these systems are monitored by humans, making it very challenging to keep an accurate account of traffic congestion, vehicle numbers, etc. Without the right traffic management protocols, even a slight increase in traffic can lead to a drastic escalation in the number of accidents and traffic jams. The use of AI solutions helps you improve your decision-making, which greatly reduces the impact of accidents and recurring congestions across roadways.
Bangalore, a city in India, is one of the most traffic-congested cities in the world. Residents are constantly encountering long traffic jams, with the average speed on the road dipping to astonishing levels of 4-6 km/hour. To combat this issue, a prototype monitoring system powered by artificial intelligence has been introduced by authorities. Using this solution, traffic cameras can automatically detect and count vehicles in real-time. This data is collected and sent back to a central control system that can approximate the traffic density on the road. According to the calculation, the system will change the traffic light based on real-time road congestion. From this example, it is quite evident that real-time video analytics can potentially help in reducing traffic congestion to a large extent. However, the applications of this technology don’t end here. Before moving on to the use cases of real-time video analytics, let us walk you through the basics.
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Simply put, real-time video analytics refers to the use of machine learning algorithms to automatically detect spatial or temporal events in video footage in real-time. Such solutions can help you recognize and identify pre-determined actions or events. On detection, they will be able to send alerts or notifications to the concerned authorities. In addition to this, they can also provide in-depth insights into these events, which can help businesses think and act proactively.
Common events that a video analytics solution can identify vehicles that are not following traffic rules, a sudden burst of smoke or flame, suspicious individual loitering around the campus, and more. Generally, it is used in three different ways:
To know more about video analytics, you can take a look at our article ‘How real-time video analytics can benefit your organization?’
The architecture of a video analytics solution depends greatly on its potential application. However, its general scheme always remains the same. A video analytics solution is normally used in two different ways. The first method is by configuring the system to send a notification each time when a specific occurrence happens. The second one is to perform advanced searches for facilitating forensic analysis in post-processing.
All of the data that is analyzed by the video analytics solution comes from myriad streaming video sources. Some of the most frequently used sources include CCTV footage, traffic cameras, and video feeds. However, most video sources that make use of the appropriate protocol (e.g. RTSP: real-time streaming protocol or HTTP) can usually be integrated into the solution. In addition to this, we can also obtain data from traffic monitoring systems, road infrastructure, cars, and drivers themselves via their mobile phones. One of the most important aspects of this process is coverage- it is essential to have a complete view of the entire area. It is also ideal to have footage of the event from different angles. This can help the system understand the situation much better.
Once the data has been collected, the video analytics solution will use machine learning algorithms that are previously trained on similar data sets to count the number of vehicles in an area and identify them. Once the analysis has been completed, a real-time notification or alert can be sent real-time to the concerned authorities.
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Video analytics solutions are typically run on servers called central processing servers. On the other hand, they can be embedded in the cameras themselves, a concept called edge processing. The ideal practice would be to use real-time edge processing on cameras and forensic analysis functionalities on the central server. Nowadays, we can see many solutions that combine both central processing and edge processing. These solutions are called hybrid solutions.
By using a hybrid approach, the processing power needed by the camera is reduced. Since the volume of data that needs to be processed on the central server is greatly minimized. Furthermore, it is possible to program the solution to send data about suspicious events to the server over the network, reducing network traffic and the need for storage.
Real-time vehicle counting and analytics have myriad applications in several industries. Some of the major use cases can be seen as follows:
All of these solutions can also be equipped with an online dashboard. This dashboard will have access to all of the historical data obtained by the system on a daily, weekly, monthly view, dynamic statistics generated by time period or type of element such as several vehicles, people per hour, day.
Some of the commonly observed benefits include:
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Real-time vehicle counting systems can be used in a variety of industries. From road management, parking plazas, toll booths, smart cities, logistics, ports, etc. Insights into factors like daily volume count, travel times calculation, and traffic forecasts are all precursors of a reliable AI system. Such parameters are a key component in optimizing traffic on various highways and roads. Information on vehicle counts can also help engineers acquire future traffic forecasts, which can help identify what routes are used by vehicles more. This can aid planning decisions during road construction.
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