Micro-expression spotting is a fundamental step in the micro-expression analysis. This paper proposes a novel network based convolutional neural network (CNN) for spotting multi-scale spontaneous micro-expression intervals in long videos. We named the network as Micro-Expression Spotting Network (MESNet). It is composed of three modules. The first module is a 2+1D Spatiotemporal Convolutional Network, which uses 2D convolution to extract spatial features and 1D convolution to extract temporal features. The second module is a Clip Proposal Network, which gives some proposed micro-expression clips. The last module is a Classification Regression Network, which classifies the proposed clips to micro-expression or not, and further regresses their temporal boundaries. We also propose a novel evaluation metric for spotting micro-expression. Extensive experiments have been conducted on the two long video datasets: CAS(ME) ² and SAMM, and the leave-one-subject-out cross-validation is used to evaluate the spotting performance. Results show that the proposed MESNet effectively enhances the F1-score metric. And comparative results show the proposed MESNet has achieved a good performance, which outperforms other state-of-the-art methods, especially in the SAMM dataset.

Public datasets are valuable for the development of the learning-based algorithms. In the field of autonomous driving, almost all the existing public datasets are collected by manually driving cars, and most of them are contributed by American and European researchers and represent the traffic scenarios typical for these countries. To diversify the public driving datasets, and to provide data support for researches on the self-driving vehicles moving on bike lanes such as wheelchairs, bikes and some robots, we present the CDBV (Chinese Driving from a Bike View), which is a driving dataset collected by a bike in Beijing, China. The CDBV dataset contains annotated images, raw videos and code tools. The annotations are for the object detection tasks, and especially focus on the categories with Chinese characteristics including the express tricycles, the motorbikes specializing in takeouts, the bicycle landmarks, the zebra crossings and the traffic lights. We totally annotated 40064 traffic elements in 13427 images. The CDBV dataset is publicly available at http://sujingwang.name/CDBV.html.