A survey of datasets for visual tracking <s> Dataset References <s> This paper introduces a new video surveillance dataset that was captured by a network of synchronized cameras placed throughout an indoor setting and augmented with groundtruth data. The dataset includes ten minutes of individuals who are moving throughout the sensor network. In addition, three scripted scenarios that contain behaviors exhibtied over a wide-area, such as "gathering for a meeting" or "stealing an object" are included to assist researchers who are interested in wide-area surveillance and behavior recognition. In addition to the video data, a face and gait database for all twelve individuals observed by the network of cameras is supplied. Hand-segmented ground-truth foreground regions are provided for every 500th frame in all cameras and for many sequential frames in two overlapping views. The entrance and exit time of each individual in each camera for one of the scenarios is provided in an XML database. We believe that the dataset will help provide a common development and verifcation framework for the increasing number of research efforts related to video surveillance in multiple, potentially non-overlapping, camera networks. <s> BIB001 </s> A survey of datasets for visual tracking <s> Dataset References <s> Although there is much research on behaviour recognition in time-varying video, there are few ground truthed datasets for assessing multi-person behavioral interactions. This short paper presents the BEHAVE project’s dataset, which has around 90,000 frames of humans identified by bounding boxes, with interacting groups classified into one of 5 different behaviors. An example of its use is also presented. <s> BIB002 </s> A survey of datasets for visual tracking <s> Dataset References <s> With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion. <s> BIB003 </s> A survey of datasets for visual tracking <s> Dataset References <s> This paper describes the world's largest gait database-the “OU-ISIR Gait Database, Large Population Dataset”-and its application to a statistically reliable performance evaluation of vision-based gait recognition. Whereas existing gait databases include at most 185 subjects, we construct a larger gait database that includes 4007 subjects (2135 males and 1872 females) with ages ranging from 1 to 94 years. The dataset allows us to determine statistically significant performance differences between currently proposed gait features. In addition, the dependences of gait-recognition performance on gender and age group are investigated and the results provide several novel insights, such as the gradual change in recognition performance with human growth. <s> BIB004 </s> A survey of datasets for visual tracking <s> Dataset References <s> The recognition of continuous natural gestures is a complex and challenging problem due to the multi-modal nature of involved visual cues (e.g. fingers and lips movements, subtle facial expressions, body pose, etc.), as well as technical limitations such as spatial and temporal resolution and unreliable depth cues. In order to promote the research advance on this field, we organized a challenge on multi-modal gesture recognition. We made available a large video database of 13,858 gestures from a lexicon of 20 Italian gesture categories recorded with a Kinect™ camera, providing the audio, skeletal model, user mask, RGB and depth images. The focus of the challenge was on user independent multiple gesture learning. There are no resting positions and the gestures are performed in continuous sequences lasting 1-2 minutes, containing between 8 and 20 gesture instances in each sequence. As a result, the dataset contains around 1.720.800 frames. In addition to the 20 main gesture categories, "distracter" gestures are included, meaning that additional audio and gestures out of the vocabulary are included. The final evaluation of the challenge was defined in terms of the Levenshtein edit distance, where the goal was to indicate the real order of gestures within the sequence. 54 international teams participated in the challenge, and outstanding results were obtained by the first ranked participants. <s> BIB005 </s> A survey of datasets for visual tracking <s> Dataset References <s> Action recognition in unconstrained situations is a difficult task, suffering from massive intra-class variations. It is made even more challenging when complex 3D actions are projected down to the image plane, losing a great deal of information. The recent emergence of 3D data, both in broadcast content, and commercial depth sensors, provides the possibility to overcome this issue. This paper presents a new 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. Our evaluation compares all 4 feature descriptors, using 7 different types of interest point, over a variety of threshold levels, for the Hollywood3D dataset. We make the dataset including stereo video, estimated depth maps and all code required to reproduce the benchmark results, available to the wider community. <s> BIB006
Hand gesture Thomas Moeslund's gesture recognition database  Sign language recognition dataset  3D iconic gesture dataset  Cambridge hand gesture data set  Multi-modal gesture recognition dataset [122] Pointing gestures: video sequence database  Two-handed datasets [124] Kinect gesture data set [125] MSR action recognition datasets  Multi-modal Gesture Recognition Challenge 2013 BIB005 ChAirGest'13 dataset [128] Face The Bircheld dataset [10] Incremental learning for robust visual tracking project website The Honda/UCSD video database [129] RS-DMV dataset [130] CLEAR 2007 evaluation datasets  Head pose image database [132] CAVA database [133] YouTube celebrities face tracking and recognition dataset  Automatic naming of characters in TV video [135] Hannah dataset [136] The big bang theory dataset  SARC3D Cohn-Kanade AU-coded expression database [142] MMI facial expression database [143] Facial expressions in the wild (SFEW / AFEW) [144] Biwi 3D audiovisual corpus of affective communication -B3D(AC)2  Facial expressions and emotion database [146] RS-DMV dataset [130] The UNBC-McMaster shoulder pain expression archive database [147] Body motion CLEAR 2007 evaluation datasets  Buffy stickmen dataset [148] ETHZ pascal stickmen dataset [149] ICPR '12 contest [150] HumanEva dataset  Ultrech multi-person motion benchmark [152] The OU-ISIR gait database BIB004 WVU outdoor SWIR gait dataset  DGait database [159] Action/activity Laptev's dataset [160] i3DPost multi-view human action datasets Weisman's dataset [162] CASIA action database [163] KTH multi view football dataset [164] IXMAS actions dataset  Hollywood datasets [166] HMDB: A large video database for human motion recognition BIB003 TREC video retrieval evaluation: TRECVID [168] The LIRIS human activities dataset  G3D: A gaming action dataset  Hollywood 3D dataset BIB006 CMU Graphics Lab motion capture database [154] INRIA Xmas motion acquisition sequence  MuHAVi: Multicamera human action video data  WVU multi-view action recognition dataset  Berkeley multimodal human action database (MHAD) [175] UCF-ARG dataset [115] VIRAT dataset  University UCF sports action data set [181] Olympic sports dataset [182] UIUC datasets [183] Behavior Violent scenes dataset [184] Multimodal dyadic behavior dataset (MMDB) [185] BINED dataset: Belfast natural induced emotion datasets  SSPNet conflict corpus [187] Canal 9 political debates dataset [188] The BEHAVE video dataset BIB002 SDHA dataset [190] AMI meeting corpus  TV human interactions dataset [192] TalkingHeads dataset [193] CoffeeBreak dataset [194] IGCLab 6 dataset [195] USAA dataset [196] JPL first-person interaction dataset  CMU Graphics Lab motion capture database [154] Collective activity dataset [197] TA2 database [198] WOLF dataset [199] Video surveillance ETISEO dataset [200] ETH Zurik datasets [201] MIT Traffic data set  QMUL junction dataset  Public dataset of traffic video The Terrascope dataset BIB001 Airport dataset [206] KIT AIS dataset [207] Event International workshop on performance evaluation of tracking and surveillance website [97] i-Lids bag and vehicle detection challenge [208] CANDELA dataset [209] UCSD anomaly detection dataset [210] BOSS dataset [211] Multiple cameras fall dataset [212] Anomalous behavior data set [213] Crowd TUD campus and crossing datasets [214] Mall dataset [215] ETH-Person datasets [216] Edinburgh informatics forum pedestrian database  VABENE dataset [217] IPSU HUB dataset [218] Crowd segmentation data set  Collective motion database [220] OTCBVS benchmark dataset collection  Bedre brug af hallen dataset [221] Crowd counting dataset Multiple ant tracking dataset  Sport games APIDIS basket-ball dataset [229] CVBASE'06 dataset  Multi-camera and virtual PTZ dataset [231] Hocker players dataset [232] interactions or human-object interactions  [122]  [124] . The most recent datasets have been acquired by RGB-D sensors [122, 125,  . Each year, different challenges are proposed to compare algorithms, and those provide new gesture datasets BIB005 128] .