Look and learn: forcing attention through input-fusing of important features and raw image data

Lynne Grewe; Ankush Mahajan; Dikshant Pravin Jain; Jake Shahshahani

doi:10.1117/12.2663206

14 June 2023 Look and learn: forcing attention through input-fusing of important features and raw image data

Lynne Grewe, Ankush Mahajan, Dikshant Pravin Jain, Jake Shahshahani

Proceedings Volume 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII; 125470R (2023) https://doi.org/10.1117/12.2663206
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

This work examines how a forced-attention technique can be applied to the task of Video Activity Recognition. The Look & Learn system performs early fusion of critical detected areas of attention with the original raw image data for training a system for video activity recognition, specifically the task of Squat “Quality” Detection. Look & Learn is compared to previous work, USquat, and achieved a 98.96% accuracy on average compared to the USquat system which achieved 93.75% accuracy demonstrating the improvement that can be gained by Look & Learn’s forced-attention technique. Look & Learn is deployed in an Android Application for proof of concept and results presented.

Conference Presentation

Citation Download Citation

Lynne Grewe, Ankush Mahajan, Dikshant Pravin Jain, and Jake Shahshahani "Look and learn: forcing attention through input-fusing of important features and raw image data", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 125470R (14 June 2023); https://doi.org/10.1117/12.2663206

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