On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts

Alejandro Gonzalez, David Vazquez, Antonio M. Lopez, Jaume Amores

Producció científica: Contribució a una revistaArticleRecercaAvaluat per experts

77 Cites (Scopus)

Resum

© 2013 IEEE. Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient.
Idioma originalEnglish
Número d’article7533479
Pàgines (de-a)3980-3990
RevistaIEEE Transactions on Cybernetics
Volum47
Número11
DOIs
Estat de la publicacióPublicada - 1 de nov. 2017

Fingerprint

Navegar pels temes de recerca de 'On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts'. Junts formen un fingerprint únic.

Com citar-ho