Action Detection

UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240. Source: Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification Image Source: https://www.crcv.ucf.edu/data/UCF101.php

The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance. Source: Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors Image Source: https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database

The ActivityNet dataset contains 200 different types of activities and a total of 849 hours of videos collected from YouTube. ActivityNet is the largest benchmark for temporal activity detection to date in terms of both the number of activity categories and number of videos, making the task particularly challenging. Version 1.3 of the dataset contains 19994 untrimmed videos in total and is divided into three disjoint subsets, training, validation, and testing by a ratio of 2:1:1. On average, each activity category has 137 untrimmed videos. Each video on average has 1.41 activities which are annotated with temporal boundaries. The ground-truth annotations of test videos are not public. Source: Dynamic Temporal Pyramid Network: A Closer Look at Multi-Scale Modeling for Activity Detection

The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. Source: https://arxiv.org/abs/1705.06950 Image source: https://arxiv.org/abs/1705.06950

NTU RGB+D is a large-scale dataset for RGB-D human action recognition. It involves 56,880 samples of 60 action classes collected from 40 subjects. The actions can be generally divided into three categories: 40 daily actions (e.g., drinking, eating, reading), nine health-related actions (e.g., sneezing, staggering, falling down), and 11 mutual actions (e.g., punching, kicking, hugging). These actions take place under 17 different scene conditions corresponding to 17 video sequences (i.e., S001–S017). The actions were captured using three cameras with different horizontal imaging viewpoints, namely, −45∘,0∘, and +45∘. Multi-modality information is provided for action characterization, including depth maps, 3D skeleton joint position, RGB frames, and infrared sequences. The performance evaluation is performed by a cross-subject test that split the 40 subjects into training and test groups, and by a cross-view test that employed one camera (+45∘) for testing, and the other two cameras for training. Source: Action Recognition for Depth Video using Multi-view Dynamic Images

The Charades dataset is composed of 9,848 videos of daily indoors activities with an average length of 30 seconds, involving interactions with 46 objects classes in 15 types of indoor scenes and containing a vocabulary of 30 verbs leading to 157 action classes. Each video in this dataset is annotated by multiple free-text descriptions, action labels, action intervals and classes of interacting objects. 267 different users were presented with a sentence, which includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence. In total, the dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos. In the standard split there are7,986 training video and 1,863 validation video. Source: Temporal Reasoning Graph for Activity Recognition

The THUMOS14 (THUMOS 2014) dataset is a large-scale video dataset that includes 1,010 videos for validation and 1,574 videos for testing from 20 classes. Among all the videos, there are 220 and 212 videos with temporal annotations in validation and testing set, respectively. Source: Learning to Localize Actions from Moments Image Source: http://crcv.ucf.edu/THUMOS14/

The Human Activity Recognition Dataset has been collected from 30 subjects performing six different activities (Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). It consists of inertial sensor data that was collected using a smartphone carried by the subjects. Source: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Image Source: https://www.youtube.com/watch?v=XOEN9W05_4A

JHMDB is an action recognition dataset that consists of 960 video sequences belonging to 21 actions. It is a subset of the larger HMDB51 dataset collected from digitized movies and YouTube videos. The dataset contains video and annotation for puppet flow per frame (approximated optimal flow on the person), puppet mask per frame, joint positions per frame, action label per clip and meta label per clip (camera motion, visible body parts, camera viewpoint, number of people, video quality). Source: Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss Image Source: https://arxiv.org/pdf/1712.06316.pdf

The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities (such as walking, cycling, playing soccer, etc.), performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. The dataset can be used for activity recognition and intensity estimation, while developing and applying algorithms of data processing, segmentation, feature extraction and classification. ** Sensors ** 3 Colibri wireless inertial measurement units (IMU): - sampling frequency: 100Hz - position of the sensors: - 1 IMU over the wrist on the dominant arm - 1 IMU on the chest - 1 IMU on the dominant side's ankle HR-monitor: - sampling frequency: ~9Hz ** Data collection protocol ** Each of the subjects had to follow a protocol, containing 12 different activities. The folder Protocol contains these recordings by subject. Furthermore, some of the subjects also performed a few optional activities. The folder Optional contains these recordings by subject. ** Data files ** Raw sensory data can be found in space-separated text-files (.dat), 1 data file per subject per session (protocol or optional). Missing values are indicated with NaN. One line in the data files correspond to one timestamped and labeled instance of sensory data. The data files contain 54 columns: each line consists of a timestamp, an activity label (the ground truth) and 52 attributes of raw sensory data. Source: UCI

NTU RGB+D 120 is a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. Source: NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

The Georgia Tech Egocentric Activities (GTEA) dataset contains seven types of daily activities such as making sandwich, tea, or coffee. Each activity is performed by four different people, thus totally 28 videos. For each video, there are about 20 fine-grained action instances such as take bread, pour ketchup, in approximately one minute. Source: TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation Image Source: http://cbs.ic.gatech.edu/fpv/

AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity. Each of the video clips has been exhaustively annotated by human annotators, and together they represent a rich variety of scenes, recording conditions, and expressions of human activity. There are annotations for: - Kinetics (AVA-Kinetics) - a crossover between AVA and Kinetics. In order to provide localized action labels on a wider variety of visual scenes, authors provide AVA action labels on videos from Kinetics-700, nearly doubling the number of total annotations, and increasing the number of unique videos by over 500x. - Actions (AvA Actions) - the AVA dataset densely annotates 80 atomic visual actions in 430 15-minute movie clips, where actions are localized in space and time, resulting in 1.62M action labels with multiple labels per human occurring frequently. - Spoken Activity (AVA ActiveSpeaker, AVA Speech). AVA ActiveSpeaker: associates speaking activity with a visible face, on the AVA v1.0 videos, resulting in 3.65 million frames labeled across ~39K face tracks. AVA Speech densely annotates audio-based speech activity in AVA v1.0 videos, and explicitly labels 3 background noise conditions, resulting in ~46K labeled segments spanning 45 hours of data. Image Source: https://www.researchgate.net/profile/Paolo_Napoletano/publication/309327222/figure/fig1/AS:419620126248965@1477056642346/Sample-images-from-the-Aesthetic-Visual-Analysis-AVA-database-sorted-by-their-aesthetic.png

The Penn Action Dataset contains 2326 video sequences of 15 different actions and human joint annotations for each sequence. Source: http://dreamdragon.github.io/PennAction/ Image Source: http://dreamdragon.github.io/PennAction/

The PKU-MMD dataset is a large skeleton-based action detection dataset. It contains 1076 long untrimmed video sequences performed by 66 subjects in three camera views. 51 action categories are annotated, resulting almost 20,000 action instances and 5.4 million frames in total. Similar to NTU RGB+D, there are also two recommended evaluate protocols, i.e. cross-subject and cross-view. Source: Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation Image Source: https://www.icst.pku.edu.cn/struct/Projects/PKUMMD.html

The UT-Kinect dataset is a dataset for action recognition from depth sequences. The videos were captured using a single stationary Kinect. There are 10 action types: walk, sit down, stand up, pick up, carry, throw, push, pull, wave hands, clap hands. There are 10 subjects, Each subject performs each actions twice. Three channels were recorded: RGB, depth and skeleton joint locations. The three channel are synchronized. The framerate is 30f/s. Source: https://cvrc.ece.utexas.edu/KinectDatasets/HOJ3D.html Image Source: https://cvrc.ece.utexas.edu/KinectDatasets/HOJ3D.html

The CAD-60 and CAD-120 data sets comprise of RGB-D video sequences of humans performing activities which are recording using the Microsoft Kinect sensor. Being able to detect human activities is important for making personal assistant robots useful in performing assistive tasks. The CAD dataset comprises twelve different activities (composed of several sub-activities) performed by four people in different environments, such as a kitchen, a living room, and office, etc. Source: https://www.re3data.org/repository/r3d100012216 Image Source: https://www.researchgate.net/figure/The-CAD-120-dataset-A-Examples-of-high-level-activities-from-the-dataset-B-A_fig3_335424041

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

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.

CholecT50 is a dataset of endoscopic videos of laparoscopic cholecystectomy surgery introduced to enable research on fine-grained action recognition in laparoscopic surgery. It is annotated with triplet information in the form of <instrument, verb, target>. The dataset is a collection of 50 videos consisting of 45 videos from the Cholec80 dataset and 5 videos from an in-house dataset of the same surgical procedure. CholecT50 is an extension of CholecT40 with 10 additional videos and standardized classes. CholecT45 is a subset of CholecT50 consisting of 45 videos from the Cholec80 dataset and first public release of CholecT50. The following are the official variants of the dataset: - 1. CholecT50 : the original version as used in the Rendezvous publication. - ** 2.CholecT50 (challenge)** : the variant used in CholecTriplet challenge. - ** 3. CholecT50 (cross-val): official cross validation split of CholecT50. - ** 4. CholecT45 (cross-val): official cross validation split of CholecT45. - ** 5. CholecT40** : the original version of CholecT40 as used in Tripnet publication. The complete dataset split information is given here.

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

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/

A large scale dataset with daily-living activities performed in a natural manner. Source: Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection

The Multiview 3D event dataset is capture by me and Xiaohan Nie in UCLA. it contains RGB, depth and human skeleton data captured simultaneously by three Kinect cameras. This dataset include 10 action categories: pick up with one hand, pick up with two hands, drop trash, walk around, sit down, stand up, donning, doffing, throw, carry. Each action is performed by 10 actors. This dataset contains data taken from a variety of viewpoints. The dataset can be found in part-1, part-2 part-3, part-4, part-5, part-6, part-7, part-8, part-9, part-10, part-11, part-12, part-13, part-14, part-15, part-16, We also created a version of the dataset that only contains RGB videos: RGB videos only.

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

The Gaming 3D Dataset (G3D) focuses on real-time action recognition in a gaming scenario. It contains 10 subjects performing 20 gaming actions: “punch right”, “punch left”, “kick right”, “kick left”, “defend”, “golf swing”, “tennis swing forehand”, “tennis swing backhand”, “tennis serve”, “throw bowling ball”, “aim and fire gun”, “walk”, “run”, “jump”, “climb”, “crouch”, “steer a car”, “wave”, “flap” and “clap”. Source: Skeleton Based Action Recognition Using Translation-Scale Invariant Image Mapping And Multi-Scale Deep CNN Image Source: G3D: A gaming action dataset and real time action recognition evaluation framework

SBU-Kinect-Interaction dataset version 2.0 comprises of RGB-D video sequences of humans performing interaction activities that are recording using the Microsoft Kinect sensor. This dataset was originally recorded for a class project, and it must be used only for the purposes of research. If you use this dataset in your work, please cite the following paper. Kiwon Yun, Jean Honorio, Debaleena Chattopadhyay, Tamara L. Berg, and Dimitris Samaras, The 2nd International Workshop on Human Activity Understanding from 3D Data at Conference on Computer Vision and Pattern Recognition (HAU3D-CVPRW), CVPR 2012 SBU-Refine: SBU-Refine relabels the test set manually and refines the noise labels in training set by algorithm. H. Yang, T. Wang, X. Hu, and C.-W. Fu, “SILT: Shadow-aware iterative label tuning for learning to detect shadows from noisy labels,” in ICCV, 2023, pp. 12 687–12 698.

ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event.

The Drive&Act dataset is a state of the art multi modal benchmark for driver behavior recognition. The dataset includes 3D skeletons in addition to frame-wise hierarchical labels of 9.6 Million frames captured by 6 different views and 3 modalities (RGB, IR and depth). It offers following key features: * 12h of video data in 29 long sequences * Calibrated multi view camera system with 5 views * Multi modal videos: NIR, Depth and Color data * Markerless motion capture: 3D Body Pose and Head Pose * Model of the static interior of the car * 83 manually annotated hierarchical activity labels: * Level 1: Long running tasks (12) * Level 2: Semantic actions (34) * Level 3: Object Interaction tripplets [action|object|location] (6|17|14) Source: Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles

FineAction contains 103K temporal instances of 106 action categories, annotated in 17K untrimmed videos. FineAction introduces new opportunities and challenges for temporal action localization, thanks to its distinct characteristics of fine action classes with rich diversity, dense annotations of multiple instances, and co-occurring actions of different classes.

A new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors. Source: Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection

Contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames or about 38.5 hours of face tracks, and the corresponding audio. Source: AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection

Spatio-temporal action detection is an important and challenging problem in video understanding. The existing action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. This paper aims to present a new multi-person dataset of spatio-temporal localized sports actions, coined as MultiSports. We first analyze the important ingredients of constructing a realistic and challenging dataset for spatio-temporal action detection by proposing three criteria: (1) multi-person scenes and motion dependent identification, (2) with well-defined boundaries, (3) relatively fine-grained classes of high complexity. Based on these guidelines, we build the dataset of MultiSports v1.0 by selecting 4 sports classes, collecting 3200 video clips, and annotating 37701 action instances with 902k bounding boxes. Our dataset is characterized with important properties of high diversity, dense annotation, and high quality. Our MultiSports, with its realistic setting and detailed annotations, exposes the intrinsic challenges of spatio-temporal action detection. We hope our MultiSports can serve as a standard benchmark for spatio-temporal action detection in the future.

The dataset collected at the University of Florence during 2012, has been captured using a Kinect camera. It includes 9 activities: wave, drink from a bottle, answer phone,clap, tight lace, sit down, stand up, read watch, bow. During acquisition, 10 subjects were asked to perform the above actions for 2/3 times. This resulted in a total of 215 activity samples. Source: https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/ Image Source: https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/

Large-scale dataset for human activity recognition. Existing security datasets either focus on activity counts by aggregating public video disseminated due to its content, which typically excludes same-scene background video, or they achieve persistence by observing public areas and thus cannot control for activity content. The dataset is over 9300 hours of untrimmed, continuous video, scripted to include diverse, simultaneous activities, along with spontaneous background activity. Source: MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection