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SGE net: Video object detection with squeezed GRU and information entropy map

arXiv 2106.07224 ↗First posted 14 Jun 2021cs.CVcited by 0

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Abstract (arXiv)

Recently, deep learning based video object detection has attracted more and more attention. Compared with object detection of static images, video object detection is more challenging due to the motion of objects, while providing rich temporal information. The 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 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 3.7 contrasted with the baseline, and the number of parameters has decreased from 6.33 million to 0.67 million compared with the standard GRU.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 23 references that are in the map. First posted on arXiv 14 Jun 2021.

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References · 19

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7 more
  1. 2017Mobile Video Object Detection with Temporally-Aware Feature Mapscited by 39
  2. 2016Convolutional Gated Recurrent Networks for Video Segmentationcited by 27
  3. 2019NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentationcited by 21
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