Video Recognition

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

BosphorusSign22k is a benchmark dataset for vision-based user-independent isolated Sign Language Recognition (SLR). The dataset is based on the BosphorusSign (Camgoz et al., 2016c) corpus which was collected with the purpose of helping both linguistic and computer science communities. It contains isolated videos of Turkish Sign Language glosses from three different domains: Health, finance and commonly used everyday signs. Videos in this dataset were performed by six native signers, which makes this dataset valuable for user independent sign language studies. Source: BosphorusSign22k Sign Language Recognition Dataset

Contains video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions. Source: MIDV-2019: Challenges of the modern mobile-based document OCR

Includes challenging sequences and extensive data stratification in-terms of camera and object motion, velocity magnitudes, direction, and rotational speeds. Source: 0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera

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.

Imbalanced-MiniKinetics200 was proposed by "Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition" to evaluate varying scenarios of video long-tailed recognition. Similar to CIFAR-10/100-LT, it utilizes an imbalance factor to construct long-tailed variants of the MiniKinetics200 dataset. Imbalanced-MiniKinetics200 is a subset of Mini-Kinetics-200 consisting of 200 categories which is also a subset of Kinetics400. Both the raw frames and extracted features with ResNet50/101 are provided.

First of its kind paired win-fail action understanding dataset with samples from the following domains: “General Stunts,” “Internet Wins-Fails,” “Trick Shots,” & “Party Games.” The task is to identify successful and failed attempts at various activities. Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible.

This dataset defines a total of 11 crowd motion patterns and it is composed of over 6000 video sequences with an average length of 100 frames per sequence. This documentation presents how to download and process the Crowd-11 dataset. If you use this dataset, please cite our paper: Camille Dupont, Luis Tobias, and Bertrand Luvison. "Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis." In Computer Vision and Pattern Recognition Workshops (CVPRW), 2017. Since this dataset is a composition of web videos and already existing datasets, we ask you to download and accept licence of each source and dataset. The construction of the Crowd-11 dataset is composed of two steps: # Step 1: Retrieve videos of interest from the web and/or pre-existing datasets ## Retrieve the pre-existing datasets of interest The pre-existing datasets are: | DATASET NAME | url | $SOURCE_NAME | | ------------- |:-----------------------------------------------------------------:| -------------:| | UMN | http://mha.cs.umn.edu/proj_events.shtml#crowd | umn | | AGORASET |https://github.com/piallai/Agoraset | agoraset | | PETS | http://www.cvg.reading.ac.uk/PETS2009/a.html#s3 | pets | | HOCKEY FIGHT | http://visilab.etsii.uclm.es/personas/oscar/FightDetection/ | hockey | | MOVIES | http://visilab.etsii.uclm.es/personas/oscar/FightDetection/ | peliculas | | CUHK | http://www.ee.cuhk.edu.hk/~jshao/CUHKcrowd_files/cuhk_crowd_dataset.htm | cuhk | | WWW | http://www.ee.cuhk.edu.hk/~jshao/WWWCrowdDataset.html | www | | WORLDEXPO'10 CROWD COUNTING | http://www.ee.cuhk.edu.hk/~xgwang/expo.html | shanghai | | VIOLENT-FLOWS | http://www.openu.ac.il/home/hassner/data/violentflows/ | violent_flow | These datasets should be stored in their "existing_datasets/$SOURCE_NAME/" folder: . └── existing_datasets ├── agoraset ├── cuhk ├── hockey ├── peliculas │ ├── fights │ └── noFights ├── pets ├── shanghai ├── umn ├── violent_flow └── www ## Copy the videos of interest from the datasets of interest The list of the videos of interest is in existing_datasets_urls.csv. To extract them into the VOI folder, execute: python existing_datasets_gathering.py The VOI folder should have the following structure: . └── VOI ├── agoraset ├── cuhk ├── hockey ├── peliculas ├── pets ├── shanghai ├── umn ├── violent_flow └── www ## Download the videos of interest from the web The web sources are: | SOURCE NAME | url | $SOURCE_NAME | | ------------- |:----------------------------------:| -------------:| | YOUTUBE | https://www.youtube.com/ | youtube | | GETTYIMAGES | http://www.gettyimages.fr/ | gettyimages | | POND5 | https://www.pond5.com/ | pond5 | The list of the web urls to download is in web_urls.csv. The web_urls.csv file's structure is as follows : | $SOURCE NAME | URL | OUTPUT_NAME | TS_MULTIPLIER | | ------------- |:---------------------:| ------------:| --------------:| We do not provide the script to download them, but many tools exist to do it (pytube, urllib, etc...). Note: a few videos have a ts_multiplier field. These video are in slow motion and the ts_multiplier is provided to speed them up (cf. SETPTS option in avconv). The downloaded videos should be stored in their VOI/$SOURCE_NAME folder, which should now have the following structure: . └── VOI ├── agoraset ├── cuhk ├── gettyimages ├── hockey ├── peliculas ├── pets ├── pond5 ├── shanghai ├── umn ├── violent_flow ├── youtube └── www # Step 2: Processing original videos into the Crowd-11 dataset Once the VOI folder is complete, a preprocessing step is required in order to crop and trim the original videos into the Crowd-11 dataset. The preprocessing.csv file's structure is as follows : | Videoname | Label | Frame_start | Frame_end | Top_left | Top_right | Width | Height | $SOURCE_NAME | Scene_number | Crop_number | | ------------- |:---------:| ------------:| ----------:| ---------:| ----------:| -----:| ------:| ------------:| ------------:| -----------:| Installation: You need to have avconv installed: sudo apt-get install avconv Then, you need to install several python package. A virtualeenv installation is recommended: virtualenv -p python3 py source py/bin/activate pip install sk-video Execution (in the virtualenv): python script_formating.py

Welcome to L-SVD L-SVD is an extensive and rigorously curated video dataset aimed at transforming the field of emotion recognition. This dataset features more than 20,000 short video clips, each carefully annotated to represent a range of human emotions. L-SVD stands at the intersection of Cognitive Science, Psychology, Computer Science, and Medical Science, providing a unique tool for both research and application in these fields. ### Why L-SVD? Inspired by the revolutionary ImageNet, L-SVD aspires to be a benchmark in the emotional AI space, offering meticulously labeled and consistently processed video data to the global research community. - Rich Emotional Annotations: Our dataset encompasses a spectrum of eight emotions—Anger, Contempt, Disgust, Enjoyment, Fear, Sadness, Surprise, and Neutral—each annotated with precision. - Uniform Video Quality: We ensure uniform hue, contrast, and brightness across all videos to provide a standard quality baseline for algorithm development. - Exponential Growth and Community Driven: L-SVD is rapidly expanding, with new contributions regularly enhancing the depth and breadth of the dataset. ### Dataset Features - Detailed Emotional Range: L-SVD covers a broad array of emotional states, meticulously labeled for accurate recognition. - Pre-Processed for Excellence: All clips are pre-processed to ensure high fidelity and uniformity, setting a high standard for training data quality. - Inviting Global Contributions: We encourage contributions from researchers and practitioners around the world to help grow and diversify the dataset.

Laser powder bed fusion (LBPF) is the additive manufacturing (3D printing) process for metals. RAISE-LPBF is a large dataset on the effect of laser power and laser dot speed in 316L stainless steel bulk material. Both process parameters are independently sampled for each scan line from a continuous distribution, so interactions of different parameter choices can be investigated. Process monitoring comprises on-axis high-speed (20k FPS) video. The data can be used to derive statistical properties of LPBF, as well as to build anomaly detectors. RAISE-LPBF-Laser is the machine learning benchmark to reconstruct the laser parameters of the RAISE-LBPF dataset. Paper: https://doi.org/10.1016/j.addlet.2023.100161