Unsupervised Learning from Narrated Instruction Videos

We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Second, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains about 800,000 frames for five different tasks that include complex interactions between people and objects, and are captured in a variety of indoor and outdoor settings. Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner, the main steps to achieve the task and locate the steps in the input videos.Comment: Appears in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016). 21 page

A trajectory clustering approach to crowd flow segmentation in videos

This work proposes a trajectory clustering-based approach for segmenting flow patterns in high density crowd videos. The goal is to produce a pixel-wise segmentation of a video sequence (static camera), where each segment corresponds to a different motion pattern. Unlike previous studies that use only motion vectors, we extract full trajectories so as to capture the complete temporal evolution of each region (block) in a video sequence. The extracted trajectories are dense, complex and often overlapping. A novel clustering algorithm is developed to group these trajectories that takes into account the information about the trajectories’ shape, location, and the density of trajectory patterns in a spatial neighborhood. Once the trajectories are clustered, final motion segments are obtained by grouping of the resulting trajectory clusters on the basis of their area of overlap, and average flow direction. The proposed method is validated on a set of crowd videos that are commonly used in this field. On comparison with several state-of-the-art techniques, our method achieves better overall accuracy