Action Anticipation

This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection also enables evaluating the "test of time" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected under the same hypotheses albeit "two years on". The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.

Extended GTEA Gaze+ EGTEA Gaze+ is a large-scale dataset for FPV actions and gaze. It subsumes GTEA Gaze+ and comes with HD videos (1280x960), audios, gaze tracking data, frame-level action annotations, and pixel-level hand masks at sampled frames. Specifically, EGTEA Gaze+ contains 28 hours (de-identified) of cooking activities from 86 unique sessions of 32 subjects. These videos come with audios and gaze tracking (30Hz). We have further provided human annotations of actions (human-object interactions) and hand masks. The action annotations include 10325 instances of fine-grained actions, such as "Cut bell pepper" or "Pour condiment (from) condiment container into salad". The hand annotations consist of 15,176 hand masks from 13,847 frames from the videos. Source: http://cbs.ic.gatech.edu/fpv/ Image Source: http://cbs.ic.gatech.edu/fpv/

Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-view action dataset, with simultaneous static (8) and egocentric (4) recordings. Sequences are annotated with more than 100K coarse and 1M fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal segmentation. Additionally, we propose a novel task of detecting mistakes. The unique recording format and rich set of annotations allow us to investigate generalization to new toys, cross-view transfer, long-tailed distributions, and pose vs. appearance. We envision that Assembly101 will serve as a new challenge to investigate various activity understanding problems. Image Source: https://assembly-101.github.io/

Ego4D is a massive-scale egocentric video dataset and benchmark suite. It offers 3,025 hours of daily life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, a host of new benchmark challenges are presented, centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, the aim is to push the frontier of first-person perception. Description from: Facebook AI Paper: Ego4D: Around the World in 3,000 Hours of Egocentric Video GitHub: https://github.com/EGO4D

A realistic dataset composed of 27 episodes from 6 popular TV series. The dataset spans over 16 hours of footage annotated with 30 action classes, totaling 6,231 action instances. Source: Online Action Detection

EgoExoLearn is a fascinating dataset designed to bridge the gap between egocentric and exocentric views of procedural activities. 1. What Is EgoExoLearn? EgoExoLearn is a large-scale dataset that emulates how humans learn by observing others. It focuses on the process of asynchronous demonstration following. Participants in the dataset record egocentric videos as they perform tasks. These videos are guided by exocentric-view demonstration videos. In simpler terms, imagine someone watching a demonstration video (from an external perspective) and then replicating the same task while recording their own point-of-view video. 2. Dataset Details: EgoExoLearn dataset spans 120 hours and covers scenarios from daily life and specialized laboratories. It contains: - Egocentric videos: These are recorded by individuals executing tasks. - Demonstration videos: These show the same tasks from an external viewpoint. - Gaze data: High-quality gaze information accompanies the videos. - Multimodal annotations: Detailed annotations provide context and insights. 3. Applications and Benchmarks: The EgoExoLearn dataset serves as a playground for modeling the human ability and thus provides a playground to bridge asynchronous procedural actions from different viewpoints. It enables new benchmarks such as: - Cross-view association: Linking actions observed from different perspectives. - Cross-view action planning: Anticipating and planning actions based on both ego and exo views. - Cross-view referenced skill assessment: Evaluating skills across viewpoints. Using Exo-view demonstrations as guidance for better ego-view skill assessment. 4. Why Is It Important? - Understanding how we map others' activities into our own point of view is a fundamental human skill. - EgoExoLearn paves the way for creating AI agents capable of seamlessly learning by observing humans in the real world. (1) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://arxiv.org/html/2403.16182v1. (2) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://egoexolearn.github.io/. (3) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://arxiv.org/abs/2403.16182. (4) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://allainews.com/item/egoexolearn-a-dataset-for-bridging-asynchronous-ego-and-exo-centric-view-of-procedural-activities-in-real-world-2024-03-26/. (5) undefined. https://github.com/OpenGVLab/EgoExoLearn/.

Covers 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. Source: VIENA2: A Driving Anticipation Dataset

Operating rooms (ORs) are complex, high-stakes environments requiring precise understanding of interactions among medical staff, tools, and equipment for enhancing surgical assistance, situational awareness, and patient safety. Current datasets fall short in scale, realism and do not capture the multimodal nature of OR scenes, limiting progress in OR modeling. To this end, we introduce MM-OR, a realistic and large-scale multimodal spatiotemporal OR dataset, and the first dataset to enable multimodal scene graph generation. MM-OR captures comprehensive OR scenes containing RGB-D data, detail views, audio, speech transcripts, robotic logs, and tracking data and is annotated with panoptic segmentations, semantic scene graphs, and downstream task labels. Further, we propose MM2SG, the first multimodal large vision-language model for scene graph generation, and through extensive experiments, demonstrate its ability to effectively leverage multimodal inputs. Together, MM-OR and MM2SG establish a new benchmark for holistic OR understanding, and open the path towards multimodal scene analysis in complex, high-stakes environments. Paper: https://arxiv.org/abs/2503.02579

Is one of the largest egocentric datasets in the object search task with eyetracking information available Source: Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks

We present a new simulated dataset for pedestrian action anticipation collected using the CARLA simulator. To generate this dataset, we place a camera sensor on the ego-vehicle in the Carla environment and set the parameters to those of the camera used to record the PIE dataset (i.e., 1920x1080, 110° FOV). Then, we compute bounding boxes for each pedestrian interacting with the ego vehicle as seen through the camera's field of view. We generated the data in two urban environments available in the CARLA simulator: Town02 and Town03. The total number of simulated pedestrians is nearly 55k, equivalent to 14M bounding boxes samples. The critical point for each pedestrian is their first point of crossing the street (in case they will eventually cross) or the last bounding box coordinates of their path in the opposite case. The crossing behavior represents 25% of the total pedestrians. We balanced the training split of the dataset to obtain labeled sequences crossing/non-crossing in equal parts. We used sequence-flipping to augment the minority class (i.e., crossing behavior in our case) and then undersampled the rest of the dataset. The result is a total of nearly 50k pedestrian sequences. Next, the pedestrian trajectory sequences were transformed into observation sequences of equal length (i.e., 0.5 seconds) with a 60% overlap for the training splits. The TTE length is between 30 and 60 frames. It resulted in a total of nearly 220k observation sequences.

Daily Activity Recordings for Artificial Intelligence (DARai, pronounced "Dahr-ree") is a multimodal, hierarchically annotated dataset constructed to understand human activities in real-world settings. DARai consists of continuous scripted and unscripted recordings of 50 participants in 10 different environments, totaling over 200 hours of data from 20 sensors including multiple camera views, depth and radar sensors, wearable inertial measurement units (IMUs), electromyography (EMG), insole pressure sensors, biomonitor sensors, and gaze tracker. To capture the complexity in human activities, DARai is annotated at three levels of hierarchy: (i) high-level activities (L1) that are independent tasks, (ii) lower-level actions (L2) that are patterns shared between activities, and (iii) fine-grained procedures (L3) that detail the exact execution steps for actions. The dataset annotations and recordings are designed so that 22.7% of L2 actions are shared between L1 activities and 14.2% of L3 procedures are shared between L2 actions. The overlap and unscripted nature of DARai allows counterfactual activities in the dataset. Experiments with various machine learning models showcase the value of DARai in uncovering important challenges in human-centered applications. Specifically, we conduct unimodal and multimodal sensor fusion experiments for recognition, temporal localization, and future action anticipation across all hierarchical annotation levels. To highlight the limitations of individual sensors, we also conduct domain-variant experiments that are enabled by DARai's multi-sensor and counterfactual activity design setup. The code, documentation, and dataset are available at the dedicated DARai website.