Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks

@article{He2018UnsupervisedMD,
  title={Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks},
  author={Zhen He and Hangen He},
  journal={Symmetry},
  year={2018},
  volume={10},
  pages={375}
}
Nowadays, video surveillance has become ubiquitous with the quick development of artificial intelligence. Multi-object detection (MOD) is a key step in video surveillance and has been widely studied for a long time. The majority of existing MOD algorithms follow the “divide and conquer” pipeline and utilize popular machine learning techniques to optimize algorithm parameters. However, this pipeline is usually suboptimal since it decomposes the MOD task into several sub-tasks and does not… Expand
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