Improving a Real-Time Object Detector With Compact Temporal Information

Martin Ahrnbom, Morten Borno Jensen, Kalle Astrom, Mikael Nilsson, Hakan Ardo, Thomas Moeslund; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 190-197

Abstract


Neural networks designed for real-time object detection have recently improved significantly, but in practice, looking at only a single RGB image at the time may not be ideal. For example, when detecting objects in videos, a foreground detection algorithm can be used to obtain compact temporal data, which can be fed into a neural network alongside RGB images. We propose an approach for doing this, based on an existing object detector, that re-uses pretrained weights for the processing of RGB images. The neural network was tested on the VIRAT dataset with annotations for object detection, a problem this approach is well suited for. The accuracy was found to improve significantly (up to 66%), with a roughly 40% increase in computational time.

Related Material


[pdf]
[bibtex]
@InProceedings{Ahrnbom_2017_ICCV,
author = {Ahrnbom, Martin and Borno Jensen, Morten and Astrom, Kalle and Nilsson, Mikael and Ardo, Hakan and Moeslund, Thomas},
title = {Improving a Real-Time Object Detector With Compact Temporal Information},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}