Action Recognition

This dataset is an OSN-transmitted (Online Social Network) version of the NIST dataset (https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation). Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the NIST dataset (https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation). Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

Something-Something-100 is a dataset split created from Something-Something V2. A total of 100 classes are selected and each comprises 100 samples. The 100 classes were split into 64, 12, and 24 non-overlapping classes to use as the meta-training set, meta-validation set, and meta-testing set, respectively. Link to exactly selected samples can be found here: https://github.com/ffmpbgrnn/CMN/tree/master/smsm-100

The TCG dataset is used to evaluate Traffic Control Gesture recognition for autonomous driving. The dataset is based on 3D body skeleton input to perform traffic control gesture classification on every time step. The dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. Source: https://arxiv.org/pdf/2007.16072.pdf Image Source: https://github.com/againerju/tcg_recognition

TinyVIRAT contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging. Source: TinyVIRAT: Low-resolution Video Action Recognition

Verse is a new dataset that augments existing multimodal datasets (COCO and TUHOI) with sense labels. Source: Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings

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

CHAIRS is a large-scale motion-captured f-AHOI dataset, consisting of 17.3 hours of versatile interactions between 46 participants and 81 articulated and rigid sittable objects. CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process, as well as realistic and physically plausible full-body interactions. Source: CHAIRS: Towards Full-Body Articulated Human-Object Interaction Image Source: https://arxiv.org/pdf/2212.10621v1.pdf

The Composable activities dataset consists of 693 videos that contain activities in 16 classes performed by 14 actors. Each activity is composed of 3 to 11 atomic actions. RGB-D data for each sequence is captured using a Microsoft Kinect sensor and estimate position of relevant body joints. The dataset provides annotations of the activity for each video and the actions for each of the four human parts (left/right arm and leg) for each frame in every video. Source: Discriminative Hierarchical Modeling of Spatio-Temporally Composable Human Activities Image Source: https://ialillo.sitios.ing.uc.cl/ActionsCVPR2014/

A novel dataset that represents complex conversational interactions between two individuals via 3D pose. 8 pairwise interactions describing 7 separate conversation based scenarios were collected using two Kinect depth sensors. Source: From Pose to Activity: Surveying Datasets and Introducing CONVERSE

DCASE2014 is an audio classification benchmark. Source: Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization

Largest, first-of-its-kind, in-the-wild, fine-grained workout/exercise posture analysis dataset, covering three different exercises: BackSquat, Barbell Row, and Overhead Press. Seven different types of exercise errors are covered. Unlabeled data is also provided to facilitate self-supervised learning.

HDM05 is a MoCap (motion capture) dataset. It contains more than three hours of systematically recorded and well-documented motion capture data in the C3D as well as in the ASF/AMC data format. HDM05 contains almost 2337 sequences with 130 motion classes performed by 5 different actors. Source: http://resources.mpi-inf.mpg.de/HDM05/ Image Source: https://arxiv.org/pdf/1908.05750.pdf

A dataset for benchmarking action recognition algorithms in natural environments, while making use of 3D information. The dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data. Source: Hollywood 3D: Recognizing Actions in 3D Natural Scenes

We consider the task of identifying human actions visible in online videos. We focus on the widely spread genre of lifestyle vlogs, which consist of videos of people performing actions while verbally describing them. Our goal is to identify if actions mentioned in the speech description of a video are visually present. We introduce a novel dataset, IfAct, consisting of 1,268 short video clips paired with sets of actions mentioned in the video transcripts, as well as manual annotations of whether the actions are visible or not. The dataset includes a total of 14,769 actions, 4,340 of which are visible.

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

A dataset dedicated to multi-object, multi-actor activity parsing. The dataset contains * Video-level labels (activities) * Segment-level labels (sub-activities) * Atomic actions (spatio-temporal scene graph) The scene graph annotations contain object/actor classes and bounding boxes, relationship annotations, and object/actor attributes.

Replay data from human players and AI agents navigating in a 3D game environment. Introduced in "Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation" [ICML 2021] to learn how to evaluate humanlike behavior in agents.

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

PETRAW data set was composed of 150 sequences of peg transfer training sessions. The objective of the peg transfer session is to transfer 6 blocks from the left to the right and back. Each block must be extracted from a peg with one hand, transferred to the other hand, and inserted in a peg at the other side of the board. All cases were acquired by a non-medical expert on the LTSI Laboratory from the University of Rennes. The data set was divided into a training data set composed of 90 cases and a test data set composed of 60 cases. A case was composed of kinematic data, a video, semantic segmentation of each frame, and workflow annotation.

UAV-GESTURE is a dataset for UAV control and gesture recognition. It is an outdoor recorded video dataset for UAV commanding signals with 13 gestures suitable for basic UAV navigation and command from general aircraft handling and helicopter handling signals. It contains 119 high-definition video clips consisting of 37,151 frames. Source: UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition

UESTC-MMEA-CL is a new multi-modal activity dataset for continual egocentric activity recognition, which is proposed to promote future studies on continual learning for first-person activity recognition in wearable applications. Our dataset provides not only vision data with auxiliary inertial sensor data but also comprehensive and complex daily activity categories for the purpose of continual learning research. UESTC-MMEA-CL comprises 30.4 hours of fully synchronized first-person video clips, acceleration stream and gyroscope data in total. There are 32 activity classes in the dataset and each class contains approximately 200 samples. We divide the samples of each class into the training set, validation set and test set according to the ratio of 7:2:1. For the continual learning evaluation, we present three settings of incremental steps, i.e., the 32 classes are divided into {16, 8, 4} incremental steps and each step contains {2, 4, 8} activity classes, respectively.

3DYoga90 is organized within a three-level label hierarchy. It stands out as one of the most comprehensive open datasets, featuring the largest collection of RGB videos and 3D skeleton sequences among publicly available resources.

Audiovisual Moments in Time (AVMIT) is a large-scale dataset of audiovisual action events. The dataset includes the annotation of 57,177 audiovisual videos from the Moments in Time dataset, each independently evaluated by 3 of 11 trained participants. Each annotation pertains to whether the labelled audiovisual action event is present and whether it is the most prominent feature of the video. The dataset also provides a curated test set of 960 videos across 16 classes, suitable for comparative experiments involving computational models and human participants, specifically when addressing research questions where audiovisual correspondence is of critical importance.

The Consented Activities of People (CAP) dataset is a fine grained activity dataset for visual AI research curated using the Visym Collector platform. The CAP dataset contains annotated videos of fine-grained activity classes of consented people. Videos are recorded from mobile devices around the world from a third person viewpoint looking down on the scene from above, containing subjects performing every day activities. Videos are annotated with bounding box tracks around the primary actor along with temporal start/end frames for each activity instance, and distributed in vipy json format. An interactive visualization and video summary is available for review in the dataset distribution site.

CholecT40 is the first endoscopic dataset introduced to enable research on fine-grained action recognition in laparoscopic surgery. It consists of 40 videos of laparoscopic cholecystectomy surgery annotated with triplet information in the form of <instrument, verb, target>. The annotations spans over 128 triplet classes that are composed from 6 classes of surgical instruments, 8 classes of action verbs, and 19 classes of surgical targets. The dataset is used as benchmark for developing deep learning solution for the recognition of surgical activities in the form of a triplet. It is first surgical data science effort to replicate activity recognition in the same level as human-object interaction (HOI) in natural vision tasks. The parent dataset is CholecT50.

Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle. By creating and sharing CVB, our aim is to develop improved models capable of recognizing all important cattle behaviors accurately and to assist other researchers and practitioners in developing and evaluating new ML models for cattle behavior classification using video data. The dataset is presented in the form of following three sub-directories. 1. raw_frames: contains 450 frames in each sub folder representing a 15 second video taken at a frame rate of 30 FPS. 2. annotations: contains the json files corresponding to the raw_frames folder. There is one json file for each video, that contains the bounding-box annotations for each cattle in the video and its associated behavior, and 3. CVB_in_AVA_format: contains the CVB data in the AVA dataset format.

Website: https://asankagp.github.io/droneaction/

HalluEditBench is a comprehensive benchmark for evaluating knowledge editing methods' effectiveness in correcting real-world hallucinations. HalluEdit features a rigorously constructed dataset spanning nine domains and 26 topics. It evaluates methods across five dimensions: Efficacy, Generalization, Portability, Locality, and Robustness.

This is a subset of Kinetics-400, introduced in Look, Listen and Learn by Relja Arandjelovic and Andrew Zisserman.

Understanding and analyzing animal behavior is increasingly essential to protect endangered animal species. However, the application of advanced computer vision techniques in this regard is minimal, which boils down to lacking large and diverse datasets for training deep models. To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation. The collected data contains vast variations such as ecological seasons, weather conditions, periods, viewpoints, and habitat scenes. So far, we retrieved at least 500K videos and 1.2 million images. Specifically, we selected and annotated 11 endangered animals for behavior understanding, including 10K video sequences for the action recognition task, 28K images for object detection, instance segmentation, and pose estimation tasks. In addition, we gathered 7K web images of the same species as source domain data for the domain adaptation task. We provide evaluation results of representative vision understanding approaches and cross-domain experiments. LoTE-Animal dataset would facilitate the community to research more advanced machine learning models and explore new tasks to aid endangered animal conservation. Our dataset will be released with the paper.

Designed to evaluate the open view classification problem under the surveillance environment. In total, MCAD contains 14,298 action samples from 18 action categories, which are performed by 20 subjects and independently recorded with 5 cameras. Source: Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking

MetaVD is a Meta Video Dataset for enhancing human action recognition datasets. It provides human-annotated relationship labels between action classes across human action recognition datasets. MetaVD is proposed in the following paper: Yuya Yoshikawa, Yutaro Shigeto, and Akikazu Takeuchi. "MetaVD: A Meta Video Dataset for enhancing human action recognition datasets." Computer Vision and Image Understanding 212 (2021): 103276. [link] MetaVD integrates the following datasets: UCF101, HMDB51, ActivityNet, STAIR Actions, Charades, Kinetics-700 This repository does NOT provide videos in the datasets. For information on how to download the videos, please refer to the website of each dataset.

MPHOI-72 is a multi-person human-object interaction dataset that can be used for a wide variety of HOI/activity recognition and pose estimation/object tracking tasks. The dataset is challenging due to many body occlusions among the humans and objects. It consists of 72 videos captured from 3 different angles at 30 fps, with totally 26,383 frames and an average length of 12 seconds. It involves 5 humans performing in pairs, 6 object types, 3 activities and 13 sub-activities. The dataset includes color video, depth video, human skeletons, human and object bounding boxes.

The MultiviewC dataset mainly contributes to multiview cattle action recognition, 3D objection detection and tracking. We build a novel synthetic dataset MultiviewC through UE4 based on real cattle video dataset which is offered by CISRO. The format of our data set has been adjusted on the basis of MultiviewX for set-up, annotation and files structure.

NTU-X is an extended version of popular NTU dataset.