BAdaCost with trained on 48x84 images with LDCF features [BdCost48LDCF]

Submitted on 13 Jan. 2018 16:38 by
Jose M. Buenaposada (Universidad Rey Juan Carlos)

Running time:0.5 s
Environment:8 cores @ 3.5 Ghz (Matlab + C/C++)

Method Description:
BAdaCost (Multiclass Cost-sensitive Boosting) with Locally Decorrelated Channel Features (LDCF) trained in 48x84 pixels resized images.
Latex Bibtex:

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).

Benchmark Easy Moderate Hard
Car (Detection) 77.93 % 67.08 % 51.15 %
Car (Orientation) 77.10 % 66.01 % 50.35 %
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2D object detection results.
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Orientation estimation results.
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