The first and still popular approach to gait recognition applies computer vision techniques to appearance-based features of walking patterns. More recently, wearable sensors have become attractive. The accelerometer is the most used one, being embedded in widespread mobile devices. Related techniques do not suffer for problems like occlusion and point of view, but for intra-subject variations caused by walking speed, ground type, shoes, etc. However, we can often recognize a person from the walking pattern, and this stimulates to search for robust features, able to sufficiently characterize this trait. This paper presents some preliminary experiments using the convolution with Gaussian kernels to extract relevant gait elements. The experiments use the large ZJU-gaitacc public dataset, and achieve improved results compared with previous works exploiting the same dataset.
Benefits of Gaussian Convolution in Gait Recognition / DE MARSICO, Maria; Mecca, Alessio. - (2018), pp. 1-8. ((Intervento presentato al convegno BIOSIG 2018 tenutosi a Darmstadt, Germania.
|Titolo:||Benefits of Gaussian Convolution in Gait Recognition|
|Data di pubblicazione:||2018|
|Citazione:||Benefits of Gaussian Convolution in Gait Recognition / DE MARSICO, Maria; Mecca, Alessio. - (2018), pp. 1-8. ((Intervento presentato al convegno BIOSIG 2018 tenutosi a Darmstadt, Germania.|
|Appartiene alla tipologia:||04b Atto di convegno in volume|