Detecting Tear Gas Canisters With Limited Training Data

Ashwin D'Cruz, Christopher Tegho, Sean Greaves, Lachlan Kermode; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 3674-3682


Human rights investigations often require triaging large volumes of open source data in order to find moments within image, or video that are relevant to a given investigation and warrant further inspection. Searching for images of tear gas usage online manually is laborious and time-consuming. In this paper, we focus on object detection models to facilitate discovery and identification of tear gas canisters for human rights monitors. For CNN based object detection to work, a large amount of training data is required, and prior to our work, a dataset of tear gas canisters did not exist. To achieve our objective, we benchmark methods for training object detectors using limited labelled data: we fine-tune different object detection models on the limited labelled data and compare performance to a few shot detector and augmentation strategies using synthetic data. We provide a dataset for evaluating and training tear gas canister detectors and show how such detectors can be deployed for a real world application such as investigating human rights violations. Our experiments show that fine-tuning state of the art detectors perform as well as the few shot detector, and including synthetic data can improve results.

Related Material

@InProceedings{D'Cruz_2022_WACV, author = {D'Cruz, Ashwin and Tegho, Christopher and Greaves, Sean and Kermode, Lachlan}, title = {Detecting Tear Gas Canisters With Limited Training Data}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {3674-3682} }