Fusion Object Detection and Action Recognition to Predict Violent Action

Nelson Rodrigues; Nuno M. C. da Costa; César Gonçalo Macedo Melo; Ali Abbasi; Jaime C. Fonseca; Paulo Cardoso; João Borges

doi:10.20944/preprints202304.1242.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 28 April 2023 / Approved: 30 April 2023 / Online: 30 April 2023 (02:50:07 CEST)

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to detect objects, namely lost/forgotten items to inform the passengers, and aggressive items to monitoring if violent actions may arise between passengers. For object detection algorithms was used public datasets (COCO and TAO) to train state-of-the-art algorithms, such as YOLOv5. For violent action detection was used the MoLa InCar dataset to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN and TSM. At the end an embedded automotive solution was used to demonstrate both methods running in real-time.

visual intelligence; object detection; image processing; action recognition; autonomous vehicles; machine learning

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Fusion Object Detection and Action Recognition to Predict Violent Action

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