Accurate object detection using memory-based models in surveillance scenes

作者：

Highlights：

• A memory-based method is proposed for accurate object detection in surveillance scenes.

• Two models imitate the mechanism of memory and prediction in our brain respectively.

• Feature learning and sequence learning are integrated in a memory-based classification model.

• A memory-based prediction model is specially designed to output the mask indicating the potential object locations.

摘要

•A memory-based method is proposed for accurate object detection in surveillance scenes.•Two models imitate the mechanism of memory and prediction in our brain respectively.•Feature learning and sequence learning are integrated in a memory-based classification model.•A memory-based prediction model is specially designed to output the mask indicating the potential object locations.

论文关键词：Convolutional neural network,Long short-term memory,Object detection

论文评审过程：Received 17 August 2016, Revised 27 December 2016, Accepted 24 January 2017, Available online 1 February 2017, Version of Record 11 February 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.01.030