A stacked meta approach for object detection to reduce false positives in highly complex videos

Davila Delgado, Juan Manuel and Barrera-Animas, Ari Yair (2024) A stacked meta approach for object detection to reduce false positives in highly complex videos. In: ICIAI 2024: 2024 the 8th International Conference on Innovation in Artificial Intelligence, 16th-18th March 2024, Tokyo, Japan.

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Abstract

False positives are a major problem when deploying object detection models in real-world conditions. Highly complex scenes are particularly difficult to process by standard object detection models. A novel meta-approach of stacked detection and the use of multiple frames to evaluate the preliminary detections is presented. The stacked approach leverages different types of architectures and performs multiple detections to reduce the number of false positives. The approach was qualitatively validated with videos taken from construction sites and compared with some of the most used architectures, i.e., Faster-RCNN and RetinaNet. Our approach can reduce the number of false positives and increase the detection accuracy.

Item Type:	Conference or Workshop Item (Paper)
Identification Number:	10.1145/3655497.3655501
Dates:	Date Event 16 March 2024 Accepted 4 August 2024 Published Online
Uncontrolled Keywords:	Computer Vision, Object detection, Synthetic approaches, Model Ensemble
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Business, Law and Social Sciences > College of Business, Digital Transformation & Entrepreneurship
Depositing User:	Gemma Tonks
Date Deposited:	16 May 2025 11:48
Last Modified:	16 May 2025 11:48
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16358

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