Name
Abstract | ||
|---|---|---|
Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-called (1st-order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector. The statistics of the 1st-order binary context features are further calculated to construct a high order co-occurrence descriptor. Combining the MOCO feature with the original image feature, we can evolve the baseline object detector to a stronger context aware detector. With the updated detector, we can continue the evolution till the contextual improvements saturate. Using the successful deformable-part-model detector [13] as the baseline detector, we test the proposed MOCO evolution framework on the PASCAL VOC 2007 dataset [8] and Caltech pedestrian dataset [7]: The proposed MOCO detector outperforms all known state-of-the-art approaches, contextually boosting deformable part models (ver. 5) [13] by 3.3% in mean average precision on the PASCAL 2007 dataset. For the Caltech pedestrian dataset, our method further reduces the log-average miss rate from 48% to 46% and the miss rate at 1 FPPI from 25% to 23%, compared with the best prior art [6]. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.235 | CVPR |
Keywords | Field | DocType |
baseline detector,caltech pedestrian dataset,image representation,cooccurrence descriptor,context feature,multiorder contextual cooccurrence,updated detector,binary context feature,proposed moco detector,image feature,high level context,context,ubiquitous computing,feature extraction,aware detector,baseline object detector,moco feature,detection evolution,object detection,detection,multi-order contextual co-occurrence,successful deformable-part-model detector,context aware detector,accuracy,detectors,context modeling | Object detection,Computer vision,Pattern recognition,Computer science,Image representation,Feature extraction,Co-occurrence,Boosting (machine learning),Artificial intelligence,Detector,Binary number | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1063-6919 |
Citations | PageRank | References |
48 | 1.30 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guang Chen | 1 | 98 | 7.51 |
| Yuanyuan Ding | 2 | 303 | 15.04 |
| Jing Xiao | 3 | 312 | 27.78 |
| Tony X. Han | 4 | 1461 | 62.13 |