Fast Simultaneous People Detection and Re-identification in a Single Shot Network

Abstract

traditional re-identification pipeline consists of a detection and re-identification step, i.e. a person detector is run on an input image to get a cutout which is then sent to a separate re-identification system. In this work we combine detection and re-identification into one single pass neural network. We propose an architecture that can do re-identification simultaneously with detection and classification. The effect of our modification has only a negligible impact on detection accuracy, and adds the calculation of re-identification vectors at virtually no cost. The resulting re-identification vector is strong enough to be used in speed sensitive applications which can benefit from an additional re-identification vector in addition to detection. We demonstrate this by using it as detection and re-identification input for a real-time person tracker. Moreover, unlike traditional detection + re-id pipelines our single-pass network’s computational cost is not dependent on the number of people in the image. ispartof: pages:49-54 ispartof: IEEE International Conference on Advanced Video and Signal-based Surveillance pages:49-54 ispartof: 15th IEEE International Conference on Advanced Video and Signal-based Surveillance location:Auckland, New Zealand date:27 Nov - 30 Nov 2018 status: published

Original document

The different versions of the original document can be found in:

• https://lirias.kuleuven.be/handle/123456789/627123

• http://xplorestaging.ieee.org/ielx7/8637085/8639076/08639489.pdf?arnumber=8639489,

http://dx.doi.org/10.1109/avss.2018.8639489

• https://lirias.kuleuven.be/retrieve/516921,

https://dblp.uni-trier.de/db/conf/avss/avss2018.html#RanstSBG18,

https://lirias.kuleuven.be/2135641,

https://academic.microsoft.com/#/detail/2911758736