TY  - JOUR
AU  - Alejandro Gonzalez Alzate
AU  - David Vazquez
AU  - Antonio Lopez
AU  - Jaume Amores
PY  - 2017//
TI  - On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts
T2  - Cyber
JO  - IEEE Transactions on cybernetics
SP  - 3980
EP  - 3990
VL  - 47
IS  - 11
KW  - Multicue
KW  - multimodal
KW  - multiview
KW  - object detection
N2  - 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.
SN  - 2168-2267
L1  - http://refbase.cvc.uab.es/files/GVL2016.pdf
UR  - http://dx.doi.org/10.1109/TCYB.2016.2593940
N1  - ADAS; 600.085; 600.082; 600.076; 600.118
ID  - Alejandro Gonzalez Alzate2017
ER  -