Unsupervised Action Segmentation

The Breakfast Actions Dataset comprises of 10 actions related to breakfast preparation, performed by 52 different individuals in 18 different kitchens. The dataset is one of the largest fully annotated datasets available. The actions are recorded “in the wild” as opposed to a single controlled lab environment. It consists of over 77 hours of video recordings. Source: https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/ Image Source: https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/

Activity recognition research has shifted focus from distinguishing full-body motion patterns to recognizing complex interactions of multiple entities. Manipulative gestures – characterized by interactions between hands, tools, and manipulable objects – frequently occur in food preparation, manufacturing, and assembly tasks, and have a variety of applications including situational support, automated supervision, and skill assessment. With the aim to stimulate research on recognizing manipulative gestures we introduce the 50 Salads dataset. It captures 25 people preparing 2 mixed salads each and contains over 4h of annotated accelerometer and RGB-D video data. Including detailed annotations, multiple sensor types, and two sequences per participant, the 50 Salads dataset may be used for research in areas such as activity recognition, activity spotting, sequence analysis, progress tracking, sensor fusion, transfer learning, and user-adaptation. The dataset includes RGB video data 640×480 pixels at 30 Hz Depth maps 640×480 pixels at 30 Hz 3-axis accelerometer data at 50 Hz of devices attached to a knife, a mixing spoon, a small spoon, a peeler, a glass, an oil bottle, and a pepper dispenser. Synchronization parameters for temporal alignment of video and accelerometer data Annotations as temporal intervals of pre- core- and post-phases of activities corresponding to steps in a recipe

A three million frame, multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human pose. Source: The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose

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 (How to : change a car tire, perform CardioPulmonary resuscitation (CPR), jump cars, repot a plant and make coffee) 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. This video presents our results of automatically discovering the scenario for the two following task : changing a tire and performing CardioPulmonary Resuscitation (CPR). At the bottom of the videos, there are three bars. The first one corresponds to our ground truth annotation. The second one corresponds to our time interval prediction in video. Finally the third one corresponds to the constraints that we obtain from the text domain. On the right, there is a list of label. They corresponds to the label recovered by our NLP method in an unsupervised manner.