Omkar2118/datasets huggingface dataset:
Youtube INRIA Instructional
Full name: 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 (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.
FollowMe Vehicle Behaviour Prediction Dataset

This dataset is a result of a study that was created to assess drivers behaviors when following a lead vehicle. The driving simulator study used a simulated suburban environment for collecting driver behavior data while following a lead vehicle driving through various unsignalized intersections. The driving environment had two lanes in each direction and a dedicated left-turn lane for the intersection. The experiment was deployed on a miniSim Driving Simulator.
We programmed the lead vehicle ran- domly turn left, right or go straight through the intersections. In total we had 2(traffic density) × 2(speed level) × 3 = 12 scenarios for each participant to be tested on.
We split the data into train, validation and test sets. The setup for the task is to observe 1 second of trajectories and predict the next 3,5 and 8 seconds.

Introducing paper: FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings