Person Layout Results: VOC2012 BETA

Competition "comp8" (train on own data)

This leaderboard shows only those submissions that have been marked as public, and so the displayed rankings should not be considered as definitive.

Average Precision (AP %)

submission date
SVM-rank-slack-RBF [?] 34.672.926.94.112-Oct-2011
HEAD-TORSO-ESTIMATOR [?] -80.9--21-Sep-2012


Head and Torso Detection with Persistent Use of HeHEAD-TORSO-ESTIMATORCanon Inc.Kan Torii, Atsushi Nogami, Kaname Tomite, Kenji Tsukamoto, Masakazu MatsuguWe build a head detector by integrating two types of detectors based on the parts-based model by Felzenszwalb et al. (PAMI 2010). One is a robust view-based head detector trained on newly annotated images in the VOC 2006-2010 trainval datasets. The other is the whole body detector of Felzenszwalb et al., although it can be configured to learn a wider variety of poses. Both detectors estimate the bounding box of the head instead of the whole body. The two detectors are integrated by merging their detections based on the score and overlap of the estimated bounding boxes. This works as a verification process of one of the detectors by the other. We can show that the total detector is also capable of estimating the inclination of the upper body.2012-09-21 12:12:49
Structured ranking for Layout DetectionSVM-rank-slack-RBFUniversity of OxfordArpit Mittal, Matthew Blaschko, Andrew Zisserman, Manuel J Marin, Phil TorrWe make use of SVM structured ranking algorithm to combine and rank the outputs of different parts detectors. Individual parts are detected using separate detectors, then, the outputs are customized to the local image using the positional and scale cues. Different part detections are finally combined using a ranking function to give a single confidence value for the human layout detection. The ranking is performed such that detections having more true-positive parts (i.e., higher precision) are returned earlier. For detection of human head, we use the parts-based model of Felzenszwalb et al. (PAMI 2010); and hand is localized using the hand detector developed by Mittal et al. (BMVC, 2011). The feet are detected using the foot part of Felzenszwalb et al.'s human detector and also returned as the bounding box around the super-pixels resembling human foot in the lower bracket of the human ROI. We use slack rescaled variant of SVM structured ranking algorithm and RBF kernel map.2011-10-12 23:16:36