Localized region context and object feature fusion for people head detection

@article{Li2016LocalizedRC,
  title={Localized region context and object feature fusion for people head detection},
  author={Yule Li and Yong Dou and Xinwang Liu and Teng Li},
  journal={2016 IEEE International Conference on Image Processing (ICIP)},
  year={2016},
  pages={594-598}
}
  • Yule Li, Y. Dou, Teng Li
  • Published 1 September 2016
  • Computer Science
  • 2016 IEEE International Conference on Image Processing (ICIP)
People head detection in crowded scenes is challenging due to the large variability in clothing and appearance, small scales of people, and strong partial occlusions. Traditional bottom-up proposal methods and existing region proposal network approaches suffer from either poor recall or low precision. In this paper, we propose to improve both the recall and precision of head detection of region proposal models by integrating the local head information. In specific, we first use a region… 

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References

SHOWING 1-10 OF 22 REFERENCES

Context-Aware CNNs for Person Head Detection

TLDR
This work leverage person-scene relations and propose a global CNN model trained to predict positions and scales of heads directly from the full image via energy-based model where the potentials are computed with a CNN framework.

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

TLDR
This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

TLDR
This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.

Edge Boxes: Locating Object Proposals from Edges

TLDR
A novel method for generating object bounding box proposals using edges is proposed, showing results that are significantly more accurate than the current state-of-the-art while being faster to compute.

Sample-Specific Late Fusion for Visual Category Recognition

TLDR
This paper identifies the optimal fusion weights for each sample and pushes positive samples to top positions in the fusion score rank list, and forms the problem as a L∞ norm constrained optimization problem and applies the Alternating Direction Method of Multipliers for the optimization.

Histograms of oriented gradients for human detection

  • N. DalalB. Triggs
  • Computer Science
    2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
TLDR
It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.

BING: Binarized normed gradients for objectness estimation at 300fps

TLDR
To improve localization quality of the proposals while maintaining efficiency, a novel fast segmentation method is proposed and demonstrated its effectiveness for improving BING’s localization performance, when used in multi-thresholding straddling expansion (MTSE) post-processing.

Object Detection with Discriminatively Trained Part Based Models

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

TLDR
This integrated framework for using Convolutional Networks for classification, localization and detection is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 and obtained very competitive results for the detection and classifications tasks.

End-to-End People Detection in Crowded Scenes

TLDR
This work proposes a model that is based on decoding an image into a set of people detections, which takes an image as input and directly outputs aset of distinct detection hypotheses.