Efficient HOG human detection

By Yanwei Pang, Yuan Yuan, Xuelong Li, Jing Pan. Signal Processing, 2011

347 citations recorded by the source corpus.

While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for intersecting detection windows. Another way is to utilize sub-cell based interpolation to efficiently compute the HOG features for each block. The combination of the two ways results in significant increase in detecting humans-more than five times better. To evaluate the proposed method, we have established a top-view human database. Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed method.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4a207eec746aeea4d346375ac6719dc57d085dbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Houqiang Li
2. Wen-gang Zhou
3. Jiebo Luo
4. Lei Wu
5. Xingyang Cai
6. Luc Van Gool
7. Yi Yang
8. Dacheng Tao

Citations in the corpus

• Effective Active Skeleton Representation for Low Latency Human Action Recognition (2016, background): “Object detection [31], [32] can help describe human-object interactions and human detection [33], [34] can improve local motion features by avoiding camera movement.”