Lightweight Optical Flow Estimation Method Based on Improved LiteFlowNet3
Existing optical flow estimation methods have large parameter counts and time-consuming computations,which are difficult to meet the real-time demand. To solve this problem,a lightweight optical flow estimation method based on improved LiteFlowNet3 is proposed. Pooling and depth-separable convolution are used to replace the conventional convolutional layers in LiteFlowNet3,significantly reduce the number of network model parameters. The loss of the optical flow gradient is added to the loss function to emphasize the supervision of the optical flow boundaries during training,improved the performance without increasing the parameters. The improved LiteFlowNet3 parameter count is only 0.78 M. Experimental results show that the improved LiteFlowNet3 optical flow estimation method has end-point errors of 2.69 and 4.12 on Clean and Final sequences of the Sintel dataset. Respectively,the single inference time is only 25 ms. The performance outperforms that of other lightweight optical flow methods and is highly competitive.
万方数据
维普
