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Pedestrian detection via logistic multiple instance boosting

9 years 4 months ago
Pedestrian detection via logistic multiple instance boosting
Pedestrian detection in still image should handle the large appearance and pose variations arising from the articulated structure and various clothing of human bodies as well as view points. So it is difficult to design effective classifier for this problem. In this paper, we address these variations in detection via multiple instance learning, specifically logistic multiple instance boosting (LMIB). In LMIB, a example is represented as a set of instances, which implicitly encode the variations. Giving different confidence to the instances in a bag, the LMIB will automatically reduce the influence of the variations at training stage. To obtain rapid detection speed, the LMIBs are grouped into the cascaded structure. The proposed detection algorithm is tested on MIT and INRIA human datasets where promising detection results are comparable with the baseline algorithms.
Junbiao Pang, Qingming Huang, Shuqiang Jiang, Wen
Added 20 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Junbiao Pang, Qingming Huang, Shuqiang Jiang, Wen Gao
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