After being implemented in image and video processing, deep learning gave way to a practical deployment aimed at the detection and recognition of objects, yielding acceptable results in terms of precision, allowing the development of tools aimed at calculating the number of objects. people in a crowd.
In this sense, a team of scientists belonging to the Advanced Institute of Science and Technology of Japan (JAIST) carried out the development of a new technology whose effectiveness allowed obtaining a more precise estimate of the density of objects.
The development team of this technology points out that it can be applied to calculate the human density in a public place or the density of vehicles in a road in order to establish strategies that promote the improvement of public safety and traffic efficiency.
When it comes to obtaining information in public spaces or vehicular traffic routes, video surveillance is one of the most effective measures.
Through video surveillance, the personnel in charge can obtain data about the number of people or vehicles that travel through a certain area, as well as behaviors and events associated with these that can help to carry out improvements in aspects such as safety, protection and efficiency.
This process is also referred to as crowd counting where the JAIST research group led by Dr. Sooksatra and Professor Atsuo Yoshikata in conjunction with the SIIT research group in Thailand dedicated efforts to improve its effectiveness through a network that achieved a higher performance in the technique used.
In this regard, the director of the Yoshitaka Laboratory, Atsuo Yoshitaka expressed the following The new technology allows you to take advantage of both high-level and low-level features in an image and therefore achieves higher performance than before.
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