Victor Hugo de Melo will present his research on activity recognition at SIBGRAPI 2018

Images extracted from the datasets used to train the method.

Video understanding is the next frontier of computer vision, in which activity recognition plays a major role. Ph.D. student Victor Hugo Melo will present his new approach to the subject, based on contextual cues obtained from object detections in the scene, at the main conference on computer vision in South America, the Conference on Graphics, Patterns and Images, SIBGRAPI 2018.… Read more

Activity Recognition based on Wearable Sensors

Human activity recognition based on wearable sensors has received great attention in areas such as healthcare, homeland security and smart environments, mainly because it enables easy data acquisition and processing. This task consists of assigning a category of activity to signals provided by wearable sensors such as accelerometers, gyroscopes and magnetometers.



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OFCM – Optical Flow Co-occurrence Matrices

Source code of the spatiotemporal feature descriptor proposed in the Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor (ICPR 2016). Aiming at capturing more information from the optical flow, this work proposes a novel spatiotemporal local feature descriptor called Optical Flow Co-occurrence Matrices (OFCM). The method is based on the extraction of Haralick features from co-occurrence matrices computed using the optical flow information.… Read more