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Temporal Motionless Analysis of Video using CNN in MPSoC

Somdip Dey1, Amit Kumar Sing1, Dilip Kumar Prasad2 and Klaus McDonald-Maier1

1 University of Essex, UK 2 UiT The Arctic University of Norway, Tromsø¸, Norway


This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis of videos) on mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption of the device.


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