The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Class-Specific Hough Forests for Object Detection
Abstract
We present a method for the detection of instances of an object class, such as cars or pedestrians, in natural images. Similarly to some previous works, this is accomplished via generalized Hough transform, where the detections of individual object parts cast probabilistic votes for possible locations of the centroid of the whole object; the detection hypotheses then correspond to the maxima of the Hough image that accumulates the votes from all parts. However, whereas the previous methods detect object parts using generative codebooks of part appearances, we take a more discriminative approach to object part detection. Towards this end, we train a class-specific Hough forest, which is a random forest that directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. We demonstrate that Hough forests improve the results of the Hough-transform object detection significantly and achieve state-of-the-art performance for several classes and datasets.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Sampling Strategies for Bag-of-Features Image Classification
Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927
Cited by
Deep Hough Voting for 3D Object Detection in Point Clouds
Qi, C. R., Litany, O., He, K., & Guibas, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00937 · Cited by 1,244
Vehicle detection in aerial imagery : A small target detection benchmark
Razakarivony, S., & Jurie, F. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.11.002 · Cited by 916
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Cheng, B., Collins, M. D., Zhu, Y., Liu, T., Huang, T. S., Adam, H., & Chen, L.-C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01249 · Cited by 635
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation
Pham, V.-Q., Kozakaya, T., Yamaguchi, O., & Okada, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.372 · Cited by 363
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Cai, Z., Saberian, M., & Vasconcelos, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.384 · Cited by 326
Metadata
- Authors
- Gall, J., & Lempitsky, V.
- Venue
- Advances in Computer Vision and Pattern Recognition
- Publisher
- Springer London
- Published
- 2013
- Pages
- 143-157
- ISSN
- 2191-6586 (ISSN-L)
- 2191-6594
- OpenAlex
- W1482914963
- Cited by
- 555
- References
- 4
- FWCI
- 113.44
- Type
- book chapter
- Language
- en
- Open access
- closed
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