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The UTD-MHAD dataset consists of 27 different actions performed by 8 subjects. Each subject repeated the action for 4 times, resulting in 861 action sequences in total. The RGB, depth, skeleton and the inertial sensor signals were recorded. Source: Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints for Action Recognition Image Source: https://www.researchgate.net/figure/Sample-shots-of-the-27-actions-in-the-UTD-MHAD-database_fig12_283090976
The MultiTHUMOS dataset contains dense, multilabel, frame-level action annotations for 30 hours across 400 videos in the THUMOS'14 action detection dataset. It consists of 38,690 annotations of 65 action classes, with an average of 1.5 labels per frame and 10.5 action classes per video. Source: http://ai.stanford.edu/~syyeung/everymoment.html Image Source: http://ai.stanford.edu/~syyeung/everymoment.html
A novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Source: SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
XD-Violence is a large-scale audio-visual dataset for violence detection in videos.
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/
CrossTask dataset contains instructional videos, collected for 83 different tasks. For each task an ordered list of steps with manual descriptions is provided. The dataset is divided in two parts: 18 primary and 65 related tasks. Videos for the primary tasks are collected manually and provided with annotations for temporal step boundaries. Videos for the related tasks are collected automatically and don't have annotations. Source: CrossTask Image Source: https://arxiv.org/pdf/1903.08225v2.pdf
MovieNet is a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi-modal data, e.g. trailers, photos, plot descriptions, etc.. Besides, different aspects of manual annotations are provided in MovieNet, including 1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K aligned description sentences, 65K tags of place and action, and 92 K tags of cinematic style.
A benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). Source: SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
Consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. Source: UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking Image Source: UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
There exist previous works [6, 10] that constructed referring segmentation datasets for videos. Gavrilyuk et al. [6] extended the A2D [33] and J-HMDB [9] datasets with natural sentences; the datasets focus on describing the ‘actors’ and ‘actions’ appearing in videos, therefore the instance annotations are limited to only a few object categories corresponding to the dominant ‘actors’ performing a salient ‘action’. Khoreva et al. [10] built a dataset based on DAVIS [25], but the scales are barely sufficient to learn an end-to-end model from scratch Youtube-VOS has 4,519 high-resolution videos with 94 common object categories. Each video has pixel-level instance segmentation annotation at every 5 frames in 30-fps videos, and their durations are around 3 to 6 seconds. We employed Amazon Mechanical Turk to annotate referring expressions. To ensure the quality of the annotations, we selected around 50 turkers after a validation test. Each turker was given a pair of videos, the original video and the mask-overlaid one with the target object highlighted, and was asked to provide a discriminative sentence within 20 words that describes the target object accurately. We collected two kinds of annotations, which describe the highlighted object (1) based on a whole video (Full-video expression) and (2) using only the first frame of the video (First-frame expression). After the initial annotation, we conducted verification and cleaning jobs for all annotations, and dropped objects if an object cannot be localized using language expressions only. The followings are the statistics and analysis of the two annotation types of the dataset after the verification. Full-video expression: Youtube-VOS has 6,459 and 1,063 unique objects in train and validation split, respectively. Among them, we cover 6,388 unique objects in 3,471 videos (6, 388/6, 459 = 98.9%) with 12,913 expressions in train split and 1,063 unique objects in 507 videos (1, 063/1, 063 = 100%) with 2,096 expressions in validation split. On average, each video has 3.8 language expressions and each expression has 10.0 words. First-frame expression: There are 6,006 unique objects in 3,412 videos (6, 006 /6, 459 = 93.0%) with 10,897 expressions in train split and 1,030 unique objects in 507 videos (1, 030/1, 063 = 96.9%) with 1,993 expressions in validation split. The number of annotated objects is lower than that of the full-video expressions because using only the first frame makes annotation more ambiguous and inconsistent and we dropped more annotations during the verification. On average, each video has 3.2 language expressions and each expression has 7.5 words.
The Sprites dataset contains 60 pixel color images of animated characters (sprites). There are 672 sprites, 500 for training, 100 for testing and 72 for validation. Each sprite has 20 animations and 178 images, so the full dataset has 120K images in total. There are many changes in the appearance of the sprites, they differ in their body shape, gender, hair, armor, arm type, greaves, and weapon. Source: Challenges in Disentangling Independent Factors of Variation
Rendered synthetically using a library of standard 3D objects, and tests the ability to recognize compositions of object movements that require long-term reasoning. Source: CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning Image Source: CATER
The Tumblr GIF (TGIF) dataset contains 100K animated GIFs and 120K sentences describing visual content of the animated GIFs. The animated GIFs have been collected from Tumblr, from randomly selected posts published between May and June of 2015. The dataset provides the URLs of animated GIFs. The sentences are collected via crowdsourcing, with a carefully designed annotation interface that ensures high quality dataset. There is one sentence per animated GIF for the training and validation splits, and three sentences per GIF for the test split. The dataset can be used to evaluate animated GIF/video description techniques. Source: GitHub
CoMplex video Object SEgmentation (MOSE) is a dataset to study the tracking and segmenting objects in complex environments. MOSE contains 2,149 video clips and 5,200 objects from 36 categories, with 431,725 high-quality object segmentation masks. The most notable feature of MOSE dataset is complex scenes with crowded and occluded objects. Source: MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
UAV-Human is a large dataset for human behavior understanding with UAVs. It contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. The dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. This dataset can be used for UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition.
InternVid is a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodAL understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words.
MeViS is a large-scale dataset for motion expressions guided video segmentation, which focuses on segmenting objects in video content based on a sentence describing the motion of the objects. The dataset contains numerous motion expressions to indicate target objects in complex environments.
Augments the video-description dataset TACoS with short and single sentence descriptions. Source: Coherent Multi-Sentence Video Description with Variable Level of Detail
This data set was prepared from 88 open-source YouTube cooking videos. The YouCook dataset contains videos of people cooking various recipes. The videos were downloaded from YouTube and are all in the third-person viewpoint; they represent a significantly more challenging visual problem than existing cooking and kitchen datasets (the background kitchen/scene is different for many and most videos have dynamic camera changes). In addition, frame-by-frame object and action annotations are provided for training data (as well as a number of precomputed low-level features). Finally, each video has a number of human provided natural language descriptions (on average, there are eight different descriptions per video). This dataset has been created to serve as a benchmark in describing complex real-world videos with natural language descriptions. Source: A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching
The How2Sign is a multimodal and multiview continuous American Sign Language (ASL) dataset consisting of a parallel corpus of more than 80 hours of sign language videos and a set of corresponding modalities including speech, English transcripts, and depth. A three-hour subset was further recorded in the Panoptic studio enabling detailed 3D pose estimation.
The EPIC-KITCHENS-55 dataset comprises a set of 432 egocentric videos recorded by 32 participants in their kitchens at 60fps with a head mounted camera. There is no guiding script for the participants who freely perform activities in kitchens related to cooking, food preparation or washing up among others. Each video is split into short action segments (mean duration is 3.7s) with specific start and end times and a verb and noun annotation describing the action (e.g. ‘open fridge‘). The verb classes are 125 and the noun classes 331. The dataset is divided into one train and two test splits. Source: Egocentric Hand Track and Object-based Human Action Recognition Image Source: https://epic-kitchens.github.io/2020-100
NT-VOT211 consists of 211 diverse videos, offering 211,000 well-annotated frames with 8 attributes including camera motion, deformation, fast motion, motion blur, tiny target, distractors, occlusion and out-of-view. To the best of our knowledge, it is the largest night-time tracking benchmark to-date that is specifically designed to address unique challenges such as adverse visibility, image blur, and distractors inherent to night-time tracking scenarios. Kindly click the 'Homepage' button below to be directed to our official website. There, you can easily access the independent leaderboard through the 'Evaluation on Server' section on our homepage. We offer comprehensive, step-by-step guidance on utilizing our dataset, and we also provide the annotation tools that were instrumental in the creation of this dataset.
The Query-based Video Highlights (QVHighlights) dataset is a dataset for detecting customized moments and highlights from videos given natural language (NL). It consists of over 10,000 YouTube videos, covering a wide range of topics, from everyday activities and travel in lifestyle vlog videos to social and political activities in news videos. Each video in the dataset is annotated with: (1) a human-written free-form NL query, (2) relevant moments in the video w.r.t. the query, and (3) five-point scale saliency scores for all query-relevant clips.
The Argoverse 2 Motion Forecasting Dataset is a curated collection of 250,000 scenarios for training and validation. Each scenario is 11 seconds long and contains the 2D, birds-eye-view centroid and heading of each tracked object sampled at 10 Hz. To curate this collection, we sifted through thousands of hours of driving data from our fleet of self-driving test vehicles to find the most challenging segments. We place special emphasis on kinematically and socially unusual behavior, especially when exhibited by actors relevant to the ego-vehicle’s decision-making process. Some examples of interactions captured within our dataset include: buses navigating through multi-lane intersections, vehicles yielding to pedestrians at crosswalks, and cyclists sharing dense city streets. Spanning 2,000+ km over six geographically diverse cities, Argoverse 2 covers a large geographic area. Argoverse 2 also contains a large object taxonomy with 10 non-overlapping classes that encompass a broad range of actors, both static and dynamic. In comparison to the Argoverse 1 Motion Forecasting Dataset, the scenarios in this dataset are approximately twice as long and more diverse. Together, these changes incentivize methods that perform well on extended forecast horizons, handle multiple types of dynamic objects, and ensure safety in long tail scenarios.
CelebV-HQ is a large-scale video facial attributes dataset with annotations. CelebV-HQ contains 35,666 video clips involving 15,653 identities and 83 manually labeled facial attributes covering appearance, action, and emotion. GitHub repository: https://github.com/celebv-hq/celebv-hq
MAD (Movie Audio Descriptions) is an automatically curated large-scale dataset for the task of natural language grounding in videos or natural language moment retrieval. MAD exploits available audio descriptions of mainstream movies. Such audio descriptions are redacted for visually impaired audiences and are therefore highly descriptive of the visual content being displayed. MAD contains over 384,000 natural language sentences grounded in over 1,200 hours of video, and provides a unique setup for video grounding as the visual stream is truly untrimmed with an average video duration of 110 minutes. 2 orders of magnitude longer than legacy datasets. Take a look at the paper for additional information. From the authors on availability: "Due to copyright constraints, MAD’s videos will not be publicly released. However, we will provide all necessary features for our experiments’ reproducibility and promote future research in this direction"
The Visual Object Tracking (VOT) dataset is a collection of video sequences used for evaluating and benchmarking visual object tracking algorithms. It provides a standardized platform for researchers and practitioners to assess the performance of different tracking methods.
Benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. Source: PHYRE: A New Benchmark for Physical Reasoning
MOT20 is a dataset for multiple object tracking. The dataset contains 8 challenging video sequences (4 train, 4 test) in unconstrained environments, from crowded places such as train stations, town squares and a sports stadium. Image Source: https://motchallenge.net/vis/MOT20-04
A new multimodal retrieval dataset. TVR requires systems to understand both videos and their associated subtitle (dialogue) texts, making it more realistic. The dataset contains 109K queries collected on 21.8K videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal window. Source: TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
Contains 68,536 activity instances in 68.8 hours of first and third-person video, making it one of the largest and most diverse egocentric datasets available. Charades-Ego furthermore shares activity classes, scripts, and methodology with the Charades dataset, that consist of additional 82.3 hours of third-person video with 66,500 activity instances. Source: Charades-Ego: A Large-Scale Dataset of Paired Third and First Person Videos
A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available. Source: What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
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
The SHREC dataset contains 14 dynamic gestures performed by 28 participants (all participants are right handed) and captured by the Intel RealSense short range depth camera. Each gesture is performed between 1 and 10 times by each participant in two way: using one finger and the whole hand. Therefore, the dataset is composed by 2800 sequences captured. The depth image, with a resolution of 640x480, and the coordinates of 22 joints (both in the 2D depth image space and in the 3D world space) are saved for each frame of each sequence in the dataset. Source: Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition Image Source: http://tosca.cs.technion.ac.il/book/shrec.html
Participate/Get Data. ## News - SportsMOT is used for DeeperAction@ECCV-2022. - Refer to github repo: MCG-NJU/SportsMOT for the latest info.The Microsoft Research Cambridge-12 Kinect gesture data set consists of sequences of human movements, represented as body-part locations, and the associated gesture to be recognized by the system. The data set includes 594 sequences and 719,359 frames—approximately six hours and 40 minutes—collected from 30 people performing 12 gestures. In total, there are 6,244 gesture instances. The motion files contain tracks of 20 joints estimated using the Kinect Pose Estimation pipeline. The body poses are captured at a sample rate of 30Hz with an accuracy of about two centimeters in joint positions. Source: https://pgram.com/dataset/msrc-12-kinect-gesture-data-set/
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