In this paper we present Sniffer Faster R-CNN++, an efficient Camera-LiDAR late fusion network for low complexity and accurate object detection in autonomous driving scenarios. The proposed detection network architecture operates on output candidates of any 3D detector and proposals from regional proposal network of any 2D detector to generate final prediction results. In comparison to the single modality object detection approaches, fusion based methods in many instances suffer from dissimilar data integration difficulties. On one hand, fusion based network models are complicated in nature and on the other hand they require large computational overhead and resources, processing pipelines for training and inference specially, the early fusion and deep fusion approaches. As such, we devise a late fusion network that in-cooperates pre-trained, single-modality detectors without change, performing association only at the detection level. In addition to this, lidar based method fail to detect distant object due to its sparse nature so we devise proposal refinement algorithm to jointly optimize detection candidates and assist detection for distant objects. Extensive experiments on both the 3D and 2D detection benchmark of challenging KITTI dataset illustrate that our proposed network architecture significantly improves the detection accuracy, accelerating the detection speed.
“Geolocation-Centric Information Platform for resilient Spatio-temporal Content Management,
In IoT era, the growth of data variety is driven by crossdomain
data fusion. In this paper, we advocate that “local production
for local consumption (LPLC) paradigm” can be an innovative approach
in cross-domain data fusion, and propose a new framework, geolocationcentric
information platform (GCIP) that can produce and deliver diverse
spatio-temporal content (STC). In the GCIP, (1) infrastructure-based geographic
hierarchy edge network and (2) adhoc-based STC retention system
are interplayed to provide both of geolocation-awareness and resiliency.
Then, we discussed the concepts and the technical challenges of the GCIP.
Finally, we implemented a proof-of-concepts of GCIP and demonstrated its
ecacy through practical experiments on campus IPv6 network and simulation
experiments.
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- Award ID(s):
- 1818884
- PAR ID:
- 10289951
- Date Published:
- Journal Name:
- IEICE transactions on communications
- Volume:
- E104-B
- ISSN:
- 0916-8516
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1587/transcom.2020NVI0003
