Human activity recognition from histogram of spatiotemporal depth features Online publication date: Fri, 15-Nov-2019
by Naresh Kumar
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 8, No. 4, 2019
Abstract: The recent evolution in sensor-based depth information has been developed a sounding scope to work for human activity recognition using depth image sequences. Activity recognition is having a key importance due to its advantages in several domain like surveillance systems at airport, patient monitoring system, care of elderly people, etc. This work proposed the objective and evaluations to recognise daily life human activities by spatiotemporal depth information. For this purpose, Kinect sensor is used to collect the data pertaining to a single activity performed by multiple person at a time. The spatiotemporal depth features are computed for activity recognition and support vector machine is used in classification phase. We have nine class of human actions in the database for RGB-D human activity recognition. This dataset is reconfigured from Cornell human activity and Berkeley multimodal human action databases. For multiple human action recognition, 91.38% accuracy is achieved on the synthetic dataset.
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