Fast Pedestrian Detection in Surveillance Video Based on Soft Target Training of Shallow Random Forest

@article{Kim2019FastPD,
  title={Fast Pedestrian Detection in Surveillance Video Based on Soft Target Training of Shallow Random Forest},
  author={Sangjun Kim and Sooyeong Kwak and Byoung Chul Ko},
  journal={IEEE Access},
  year={2019},
  volume={7},
  pages={12415-12426}
}
In recent years, deep learning algorithms have achieved top performances in object detection tasks. However, in real-time, systems having memory or computing limitations very wide and deep networks with numerous parameters constitute a major obstacle. In this paper, we propose a fast method for detecting pedestrians in surveillance systems having limited memory and processing units. Our proposed method applies a model compression technique based on a teacher–student framework to a random forest… CONTINUE READING

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Key Quantitative Results

  • In experiments, our proposed method achieved up to a 2.2 times faster speed and a 2.68 times higher compression rate than teacher RF and a better detection performance than several state-of-the-art methods on the Performance Evaluation of Tracking and Surveillance 2006, Town Centre, and Caltech benchmark datasets.

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