Fujitsu Laboratories has developed a technology that utilizes deep learning to recognize the positions and connections of adjacent joints in complex movements or behavior in which multiple joints move in tandem.
This makes it possible to achieve greater accuracy in recognizing, for instance, when a person performs a task like removing objects from a box. This technology successfully achieved the world’s highest accuracy against the world standard benchmark in the field of behavior recognition, with significant gains over the results achieved using conventional technologies, which don’t make use of information on neighboring joints.
By leveraging this technology to perform checks of manufacturing procedures or unsafe behavior in public spaces, Fujitsu aims to contribute to significant improvements in public safety and the workplace, helping to deliver on the promise of a safer and more secure society for all.
Fujitsu will present the details of this technology at the 25th International Conference on Pattern Recognition (ICPR 2020), which is being held online from January 10th, 2021 (Sunday) to January 15th, 2021 (Friday).
In recent years, advances in AI technology have made it possible to recognize human behavior from video images using deep learning. This technology offers a variety of promising applications in a wide range of real-world scenarios, for example, in performing checks of manufacturing procedures in factories or detecting unsafe behavior in public spaces.
In general, human behavior recognition utilizing AI relies on temporal changes in the position of each of the skeletal joints, including in the hands, elbows, and shoulders, as identifying features, which are then linked to simple movement patterns such as standing or sitting.
With time series behavior-recognition technology developed by Fujitsu Labs, Fujitsu has successfully realized highly-accurate image recognition using a deep learning model that can operate with high-accuracy even for complex behaviors in which multiple joints change in conjunction with each other, such as removing objects from a box during un-packing.