Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 47, Issue 11, Pages 3980-3990Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2593940
Keywords
Multicue; multimodal; multiview (MV); object detection
Categories
Funding
- Spanish MICINN [TRA2014-57088-C2-1-R]
- Secretaria dUniversitats i Recerca del Departament dEconomia i Coneixement de la Generalitat de Catalunya [2014-SGR-1506]
- TECNIOspring
- EU
- ACCI
- DGT [SPIP2014-01352]
- NVIDIA Corporation
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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 bench-mark, but it is built upon very simple blocks that are easy to implement and computationally efficient.
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