Adaptive Real-Time Object Detection System with HOG and CNN Features
We proudly announce the release of ARTOS 2.0 which comes with a few new features as well as a completely refactored feature extraction framework that allows for the usage of arbitrary feature extractors (e.g. extracted from a CNN).
Some of the most prominent features and changes are:
libartos
and PyARTOS
, including GUI support for evaluating models and plotting recall-precision graphs.CaffeFeatureExtractor
as an alternative to HOG. It uses the Caffe library to extract image features from the layer of a CNN which can then be used with our fast linear detector. Please refer to README.md
and the documentation of CaffeFeatureExtractor
for instructions on how to use this new feature extractor.A more complete list of changes can be found in the CHANGELOG.md
file.
However, the introduction of a new major version means that there are some backwards-incompatible changes. The following two probably are most important:
padding
parameter has been removed from the function create_detector
of the C API. The necessary amount of padding is now determined by the feature extractor automatically.C++11
-compliant compiler.ARTOS_
.A more extensive list of changes which affect existing code and applications can be found in the CHANGELOG.md
file.
This is mainly a bugfix release, which also adds the option to disable caching of positive images during training if the available RAM is very limited.
This release comes with improved background statistics computed from 32k ImageNet samples. We've also added a novel method for fast computation of such statistics, which leverages the Fourier transform.
A number of minor bugs has been fixed too. Please refer to CHANGELOG.md
for details.
Key features of this release:
README.md
for details).DPMDetection::detectMax()
, which yields just the highest scoring detection on a given image (may be useful for classification-like tasks).ModelLearnerBase
is agnostic of the concrete learning method, which may be the WHO method implemented by ModelLearner
as well as any other linear classifier.Besides that, some minor bugs and ugly memory leaks have been fixed.
Please refer to CHANGELOG.md
for details and more changes.