Automatic detection of firearms is important for enhancing security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Viewing angle variations and occlusions by the weapon’s carrier and the surrounding people, further increases the difficulty of the task. Moreover, the existing object detectors process rectangular areas, though a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges we propose an Orientation Aware Object Detector (OAOD) which has achieved improved firearm detection and localization performance.
This code is modified using Faster RCNN. We made the two phases in Faster RCNN by adopting cascade approach. Please see the setup details of Faster RCNN here. This will assist in runnig our model.
We provide necessaery files to run the test script only using our model. Download our model from this link. Put it into
.../data/faster_rcnn_models directory
Replace the cfg, test in fast_rcnn folder. Also replace the prototxt file for test with the provided one. Also put images in
.../data/demo folder
After installation and setup, to run the test file. Place it into .../tools directory:
python demo_firearms.py
Here is the arXiv link: https://arxiv.org/abs/1904.10032
Here is the web-link: http://im.itu.edu.pk/orientation-aware-firearms-detection/
Trained model: link
DATASET is available upon request [Google Form]
BIBTEX:
@article{oaod2021neuro,
title={Leveraging orientation for weakly supervised object detection with application to firearm localization},
author={Iqbal, Javed and Munir, Muhammad Akhtar and Mahmood, Arif and Ali, Afsheen Rafaqat and Ali, Mohsen},
journal={Neurocomputing},
volume={440},
pages={310--320},
year={2021},
publisher={Elsevier}
}