An important task in computer vision is object localization and recognition within images and video. Achieving real-time object localization and recognition on low-power devices is especially relevant in the context of wearable technologies. Indeed, wearable devices have a reduced size and cost and limited computational power leading to a challenging scenario for classical computer vision algorithms. This paper improves the Hough Forest approach with several contributions: a faster computation of the features and a faster evaluation of the learned model with minimal loss in accuracy. Our method is characterized by a low computational requirement and allows real-time detection on a wearable device.
Efficient hough forest object detection for low-power devices / Ciolini, Andrea; Seidenari, Lorenzo; Karaman, Svebor; Del Bimbo, Alberto. - ELETTRONICO. - (2015), pp. 1-6. (Intervento presentato al convegno 2015 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2015 tenutosi a ita nel 2015) [10.1109/ICMEW.2015.7169857].
Efficient hough forest object detection for low-power devices
Seidenari, Lorenzo;Karaman, Svebor;Del Bimbo, Alberto
2015
Abstract
An important task in computer vision is object localization and recognition within images and video. Achieving real-time object localization and recognition on low-power devices is especially relevant in the context of wearable technologies. Indeed, wearable devices have a reduced size and cost and limited computational power leading to a challenging scenario for classical computer vision algorithms. This paper improves the Hough Forest approach with several contributions: a faster computation of the features and a faster evaluation of the learned model with minimal loss in accuracy. Our method is characterized by a low computational requirement and allows real-time detection on a wearable device.File | Dimensione | Formato | |
---|---|---|---|
2015_efficient_hough_forest.pdf
Accesso chiuso
Tipologia:
Pdf editoriale (Version of record)
Licenza:
Tutti i diritti riservati
Dimensione
697.73 kB
Formato
Adobe PDF
|
697.73 kB | Adobe PDF | Richiedi una copia |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.