Traffic Statistics Deep Learning – Video Detection
The Traffic Statistics Deep Learning solution provides real-time automatic counting and classification of pedestrians and vehicles using Artificial Intelligence and Deep Learning technology.
TagMaster’s Traffic Statistics with Deep Learning, is a state-of-the-art traffic analytics system. A flexible and cost-efficient alternative to traditional technology.
Video-based Counting and Classification Solution
Deep learning based counting and classifying
TagMaster’s innovative technology, based on deep neural networks applied to video analysis, enables accurate detection of all objects of interest in video streams.
This powerful methodology provides outstanding performance and accuracy for object detection. It is very robust and resilient against any potential sources of false detection. The video analytics algorithms will not be disrupted by shadows, reflecting light, or moving vegetation.
Deep Learning Algorithms in Video Analytics
Deep Learning, a subset of Artificial Intelligence, is a training convention by which a machine is exposed to volumes of tagged data in order to “learn” to recognize and identify the same information in new data sets. Imitating the way a human is taught, Deep Learning enables technologies to more proficiently detect and identify objects based on increased exposure to information.
Driven by robust hardware infrastructure, Deep Learning enables faster analytic output, improved processing performance and increased object detection, classification, and recognition accuracy.
The Traffic Statistics solution can distinguish between several distinct types of road users, based on their specific visual appearance. The deep learning–based approach is perfect in situations with challenging contexts, such as multiple types of road users and different movement patterns, typically at a street crossing.
Vulnerable road users
The solution offers multimodal counts in a single system with a focus on vulnerable road users counting in addition to motor vehicle users. By managing bicycles, pedestrians, and scooters, this video detection is perfectly in line with new mobility trends all over the world where these soft modes take a much more significant impact in the public space.
Multimodal Deep-Learning counting
The Traffic Statistics solution can differentiate between the following classes:
Built for complex situations
The Traffic Statistics Deep Learning solution offers flexibility and scalability. It has been developed to manage complex situations, while remaining easy to maintain and non-intrusive.
Traffic Statistics Deep Learning Solution
This solution is developed by daughter company, Citilog.
Since April 2021, Citilog is part of the TagMaster group and a leader in video analytics for monitoring and surveillance systems for the transportation market. Through thousands of Citilog solutions implemented worldwide, we contribute to roads where traffic moves safely and more freely.
Today, Citilog solutions give cities and traffic management authorities a broad span of effective and cost-efficient tools for increasing road safety and mobility. Beyond detecting incidents, our solutions help improve traffic flow, optimize intersection control and simplify traffic enforcement, as well as supplying accurate and actionable traffic data for real-time use and long-term infrastructure planning.