Learning a Layout Transfer Network for Context Aware Object Detection

Tao Wang

doi:10.1109/TITS.2019.2939213

Learning a Layout Transfer Network for Context Aware Object Detection

2020-10-01

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DOI：10.1109/TITS.2019.2939213

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We present a context aware object detection method based on a retrieve-and-transform scene layout model. Given an input image, our approach first retrieves a coarse scene layout from a codebook of typical layout templates. In order to handle large layout variations, we use a variant of the spatial transformer network to transform and refine the retrieved layout, resulting in a set of interpretable and semantically meaningful feature maps of object locations and scales. The above steps are implemented as a Layout Transfer Network which we integrate into Faster RCNN to allow for joint reasoning of object detection and scene layout estimation. Extensive experiments on three public datasets verified that our approach provides consistent performance improvements to the state-of-the-art object detection baselines on a variety of challenging tasks in the traffic surveillance and the autonomous driving domains.

Keywords:

Layout

Object detection

Estimation

Context modeling

Three-dimensional displays

Cognition

Feature extraction

Object detection

context modeling

scene layout transfer

Learning a Layout Transfer Network for Context Aware Object Detection

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