  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.
