Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multi-Aspect Detection of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Adaptive mixtures of local experts
Neural Computation
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution models for object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Discriminative mixture-of-templates for viewpoint classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Detecting people using mutually consistent poselet activations
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
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The Deformable Parts Model (DPM) has recently emerged as a very useful and popular tool for tackling the intra-category diversity problem in object detection. In this paper, we summarize the key insights from our empirical analysis of the important elements constituting this detector. More specifically, we study the relationship between the role of deformable parts and the mixture model components within this detector, and understand their relative importance. First, we find that by increasing the number of components, and switching the initialization step from their aspect-ratio, left-right flipping heuristics to appearance-based clustering, considerable improvement in performance is obtained. But more intriguingly, we observed that with these new components, the part deformations can now be turned off, yet obtaining results that are almost on par with the original DPM detector.