Ms. Dalia Aljawaheri Profile

Dalia Aljawaheri

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Proceedings Article | 1 November 2024 Presentation + Paper

Zero-shot neuro-symbolic parsing of body keypoints

Dalia Aljawaheri, Gertjan Burghouts, Jan Erik van Woerden, Judith Dijk, Hugo Kuijf

Proceedings Volume 13200, 132001C (2024) https://doi.org/10.1117/12.3031487

KEYWORDS: Weapons, Video, Firearms, Image resolution, Image classification, Pose estimation, Performance modeling, Logic, Object detection, Artificial intelligence, Machine learning

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A new approach for distinguishing neutral (e.g. walking) from threatening (e.g. aiming a handgun) poses, without training, is presented. There are various AI-based models that can classify human poses, but these oftentimes do not generalize to defence scenarios. The lack of data with threatening poses makes it hard to train new models. Our approach circumvents re-training and is a zero-shot, rule-based classification method for threatening poses. We combine a pretrained body part keypoint detection model with the neuro-symbolic framework Scallop. We compare the pretrained models MMPose and YOLOv8x-pose for keypoint detection. We use images from the YouTube Gun Detection Dataset containing persons holding a weapon and label them manually as having a ‘neutral’ or ‘aiming’ pose; the latter was further subdivided into ‘aiming a handgun’ and ‘aiming a rifle’. Scallop is used to define logic-based rules for classification, using the keypoints as input: e.g. the rule ‘aiming a handgun’ includes 'hands at shoulder height’ and 'hands far away from the body'. Recall and precision results for aiming are 0.75/0.81 and 0.83/0.73, for MMPose and YOLOv8x-pose, respectively. Average recall and average precision for ‘aiming a handgun’ and ‘aiming a rifle’ are 0.78/0.36 and 0.76/0.43, for MMPose and YOLOv8x-pose, respectively. Combining neuro-symbolic AI with pretrained pose estimation techniques shows promising results for detecting threatening human poses. Performance of neutral-versus-aiming classification is similar for both approaches, however, MMPose performs better for multi-class classification. In future research, we will focus on improving rules, identifying more poses, and using videos to obtain sequences of poses or activities.

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