2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-3.5
Paper Title SGE NET: VIDEO OBJECT DETECTION WITH SQUEEZED GRU AND INFORMATION ENTROPY MAP
Authors Rui Su, Wenjing Huang, Waseda University, China; Haoyu Ma, University of California, Irvine, United States; Xiaowei Song, Southeast University, China; Jinglu Hu, Waseda University, China
SessionMLR-APPL-IP-3: Machine learning for image processing 3
LocationArea F
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, deep learning based Video object detection has attracted more and more attention. % intro task Compared with object detection of static images, video object detection is more challenging due to the motion of objects, while providing rich temporal information. RNN-based algorithm is an effective way to enhance detection performance in videos with temporal information. However, most studies in this area only focus on the accuracy while ignoring the calculation cost and the number of parameters. In this paper, we propose an efficient method that combines channel-reduced convolutional GRU Squeezed GRU, and Information Entropy map for video object detection SGE-Net. The experimental results validate the accuracy improvement, computational savings of the Squeezed GRU, and superiority of the information entropy attention mechanism on the classification performance. The mAP has increased by 3.1 contrasted with the baseline, and the number of parameters has decreased from 6.33 million to 0.67 million compared with the standard GRU.