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32 dataset results for Tracking

Object Tracking Benchmark (OTB) is a visual tracking benchmark that is widely used to evaluate the performance of a visual tracking algorithm. The dataset contains a total of 100 sequences and each is annotated frame-by-frame with bounding boxes and 11 challenge attributes. OTB-2013 dataset contains 51 sequences and the OTB-2015 dataset contains all 100 sequences of the OTB dataset.

354 PAPERS • 4 BENCHMARKS

TrackingNet

TrackingNet is a large-scale tracking dataset consisting of videos in the wild. It has a total of 30,643 videos split into 30,132 training videos and 511 testing videos, with an average of 470,9 frames.

131 PAPERS • 1 BENCHMARK

VOT2016

VOT2016 is a video dataset for visual object tracking. It contains 60 video clips and 21,646 corresponding ground truth maps with pixel-wise annotation of salient objects.

108 PAPERS • 1 BENCHMARK

PoseTrack

The PoseTrack dataset is a large-scale benchmark for multi-person pose estimation and tracking in videos. It requires not only pose estimation in single frames, but also temporal tracking across frames. It contains 514 videos including 66,374 frames in total, split into 300, 50 and 208 videos for training, validation and test set respectively. For training videos, 30 frames from the center are annotated. For validation and test videos, besides 30 frames from the center, every fourth frame is also annotated for evaluating long range articulated tracking. The annotations include 15 body keypoints location, a unique person id and a head bounding box for each person instance.

76 PAPERS • 5 BENCHMARKS

MOT15 (Multiple Object Tracking 15)

MOT2015 is a dataset for multiple object tracking. It contains 11 different indoor and outdoor scenes of public places with pedestrians as the objects of interest, where camera motion, camera angle and imaging condition vary greatly. The dataset provides detections generated by the ACF-based detector.

58 PAPERS • 5 BENCHMARKS

VOT2017 (Visual Object Tracking Challenge)

VOT2017 is a Visual Object Tracking dataset for different tasks that contains 60 short sequences annotated with 6 different attributes.

54 PAPERS • 2 BENCHMARKS

KITTI MOTS (KITTI Multi-Object Tracking and Segmentation (MOTS) Evaluation)

The Multi-Object and Segmentation (MOTS) benchmark [2] consists of 21 training sequences and 29 test sequences. It is based on the KITTI Tracking Evaluation 2012 and extends the annotations to the Multi-Object and Segmentation (MOTS) task. To this end, we added dense pixel-wise segmentation labels for every object. We evaluate submitted results using the metrics HOTA, CLEAR MOT, and MT/PT/ML. We rank methods by HOTA [1]. Our development kit and GitHub evaluation code provide details about the data format as well as utility functions for reading and writing the label files. (adapted for the segmentation case). Evaluation is performed using the code from the TrackEval repository.

24 PAPERS • 1 BENCHMARK

inD Dataset (Intersection Drone Dataset)

The inD dataset is a new dataset of naturalistic vehicle trajectories recorded at German intersections. Using a drone, typical limitations of established traffic data collection methods like occlusions are overcome. Traffic was recorded at four different locations. The trajectory for each road user and its type is extracted. Using state-of-the-art computer vision algorithms, the positional error is typically less than 10 centimetres. The dataset is applicable on many tasks such as road user prediction, driver modeling, scenario-based safety validation of automated driving systems or data-driven development of HAD system components.

23 PAPERS • NO BENCHMARKS YET

VOT2014 (Visual Object Tracking Challenge 2014)

The dataset comprises 25 short sequences showing various objects in challenging backgrounds. Eight sequences are from the VOT2013 challenge (bolt, bicycle, david, diving, gymnastics, hand, sunshade, woman). The new sequences show complementary objects and backgrounds, for example a fish underwater or a surfer riding a big wave. The sequences were chosen from a large pool of sequences using a methodology based on clustering visual features of object and background so that those 25 sequences sample evenly well the existing pool.

11 PAPERS • NO BENCHMARKS YET

PathTrack

PathTrack is a dataset for person tracking which contains more than 15,000 person trajectories in 720 sequences.

9 PAPERS • NO BENCHMARKS YET

highD Dataseth (The Highway Drone Dataset Naturalistic Trajectories of 110 500 Vehicles Recorded at German Highways)

The highD dataset is a new dataset of naturalistic vehicle trajectories recorded on German highways. Using a drone, typical limitations of established traffic data collection methods such as occlusions are overcome by the aerial perspective. Traffic was recorded at six different locations and includes more than 110 500 vehicles. Each vehicle's trajectory, including vehicle type, size and manoeuvres, is automatically extracted. Using state-of-the-art computer vision algorithms, the positioning error is typically less than ten centimeters. Although the dataset was created for the safety validation of highly automated vehicles, it is also suitable for many other tasks such as the analysis of traffic patterns or the parameterization of driver models.

6 PAPERS • NO BENCHMARKS YET

300-VW (300 Videos in the Wild)

300 Videos in the Wild (300-VW) is a dataset for evaluating facial landmark tracking algorithms in the wild. The dataset authors collected a large number of long facial videos recorded in the wild. Each video has duration of ~1 minute (at 25-30 fps). All frames have been annotated with regards to the same mark-up (i.e. set of facial landmarks) used in the 300 W competition as well (a total of 68 landmarks). The dataset includes 114 videos (circa 1 min each).

5 PAPERS • 2 BENCHMARKS

CarFusion

We provide manual annotations of 14 semantic keypoints for 100,000 car instances (sedan, suv, bus, and truck) from 53,000 images captured from 18 moving cameras at Multiple intersections in Pittsburgh, PA. Please fill the google form to get a email with the download links:

5 PAPERS • 2 BENCHMARKS

VOT2020

VOT2020 is a Visual Object Tracking benchmark for short-term tracking in RGB.

5 PAPERS • 1 BENCHMARK

Atari-HEAD

Atari-HEAD is a dataset of human actions and eye movements recorded while playing Atari videos games. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded. The gaze data was recorded using an EyeLink 1000 eye tracker at 1000Hz. The human subjects are amateur players who are familiar with the games. The human subjects were only allowed to play for 15 minutes and were required to rest for at least 15 minutes before the next trial. Data was collected from 4 subjects, 16 games, 175 15-minute trials, and a total of 2.97 million frames/demonstrations.

4 PAPERS • NO BENCHMARKS YET

REFLACX

REFLACX (Reports and eye-tracking data for localization of abnormalities in chest x-rays)

The REFLACX dataset contains eye-tracking data for 3,032 readings of chest x-rays by five radiologists. The dictated reports were transcribed and have timestamps synchronized with the eye-tracking data.

3 PAPERS • NO BENCHMARKS YET

UAVA (UAV Assistant)

The UAVA,<i>UAV-Assistant</i>, dataset is specifically designed for fostering applications which consider UAVs and humans as cooperative agents. We employ a real-world 3D scanned dataset (<a href="https://niessner.github.io/Matterport/">Matterport3D</a>), physically-based rendering, a gamiﬁed simulator for realistic drone navigation trajectory collection, to generate realistic multimodal data both from the user’s exocentric view of the drone, as well as the drone’s egocentric view.

3 PAPERS • 1 BENCHMARK

VOT2019

VOT2019 is a Visual Object Tracking benchmark for short-term tracking in RGB.

3 PAPERS • 1 BENCHMARK

WALT (Watch and Learn TimeLapse Images)

We introduce a new dataset, Watch and Learn Time-lapse (WALT), consisting of multiple (4K and 1080p) cameras capturing urban environments over a year.

3 PAPERS • 1 BENCHMARK

MMPTRACK (Multi-camera Multiple People Tracking Dataset)

Multi-camera Multiple People Tracking (MMPTRACK) dataset has about 9.6 hours of videos, with over half a million frame-wise annotations. The dataset is densely annotated, e.g., per-frame bounding boxes and person identities are available, as well as camera calibration parameters. Our dataset is recorded with 15 frames per second (FPS) in five diverse and challenging environment settings., e.g., retail, lobby, industry, cafe, and office. This is by far the largest publicly available multi-camera multiple people tracking dataset.

2 PAPERS • NO BENCHMARKS YET

SNDZoo (The Softwarised Network Data Zoo)

The softwarised network data zoo (SNDZoo) is an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. Most of the published data sets focus on, but are not limited to, the performance of virtualised network functions (VNFs). The data is collected using fully automated NFV benchmarking frameworks, such as tng-bench, developed by us or third party solutions like Gym. The collection of the presented data sets follows the general VNF benchmarking methodology described in.

2 PAPERS • NO BENCHMARKS YET

eSports Sensors Dataset

The eSports Sensors dataset contains sensor data collected from 10 players in 22 matches in League of Legends. The sensor data collected includes:

2 PAPERS • 2 BENCHMARKS

CP2A dataset (CARLA Pedestrian Action Anticipation dataset)

We present a new simulated dataset for pedestrian action anticipation collected using the CARLA simulator. To generate this dataset, we place a camera sensor on the ego-vehicle in the Carla environment and set the parameters to those of the camera used to record the PIE dataset (i.e., 1920x1080, 110° FOV). Then, we compute bounding boxes for each pedestrian interacting with the ego vehicle as seen through the camera's field of view. We generated the data in two urban environments available in the CARLA simulator: Town02 and Town03.

1 PAPER • NO BENCHMARKS YET

Capture-24

This dataset contains Axivity AX3 wrist-worn activity tracker data that were collected from 151 participants in 2014-2016 around the Oxfordshire area. Participants were asked to wear the device in daily living for a period of roughly 24 hours, amounting to a total of almost 4,000 hours. Vicon Autograph wearable cameras and Whitehall II sleep diaries were used to obtain the ground truth activities performed during the period (e.g. sitting watching TV, walking the dog, washing dishes, sleeping), resulting in more than 2,500 hours of labelled data. Accompanying code to analyse this data is available at https://github.com/activityMonitoring/capture24. The following papers describe the data collection protocol in full: i.) Gershuny J, Harms T, Doherty A, Thomas E, Milton K, Kelly P, Foster C (2020) Testing self-report time-use diaries against objective instruments in real time. Sociological Methodology doi: 10.1177/0081175019884591; ii.) Willetts M, Hollowell S, Aslett L, Holmes C, Doherty

1 PAPER • NO BENCHMARKS YET

Example EPCIS Event Chain

This is an example data set for a hypothetical electronic products supply network.

1 PAPER • NO BENCHMARKS YET

INSANE Cross-Domain UAV Data Set (Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators)

This data set contains over 600GB of multimodal data from a Mars analog mission, including accurate 6DoF outdoor ground truth, indoor-outdoor transitions with continuous cross-domain ground truth, and indoor data with Optitrack measurements as ground truth. With 26 flights and a combined distance of 2.5km, this data set provides you with various distinct challenges for testing and proofing your algorithms. The UAV carries 18 sensors, including a high-resolution navigation camera and a stereo camera with an overlapping field of view, two RTK GNSS sensors with centimeter accuracy, as well as three IMUs, placed at strategic locations: Hardware dampened at the center, off-center with a lever arm, and a 1kHz IMU rigidly attached to the UAV (in case you want to work with unfiltered data). The sensors are fully pre-calibrated, and the data set is ready to use. However, if you want to use your own calibration algorithms, then the raw calibration data is also ready for download. The cross-domai

1 PAPER • NO BENCHMARKS YET

NBA SportVU

The NBA SportVU dataset contains player and ball trajectories for 631 games from the 2015-2016 NBA season. The raw tracking data is in the JSON format, and each moment includes information about the identities of the players on the court, the identities of the teams, the period, the game clock, and the shot clock.

1 PAPER • 1 BENCHMARK

SPAVE-28G

SPAVE-28G (Signal Propagation Analyses in V2X Ecosystems (S.P.A.V.E) at 28 GHz on the NSF POWDER testbed)

Click to add a brief description of the dataset (Markdown and LaTeX enabled).

1 PAPER • NO BENCHMARKS YET

aiMotive Dataset (aiMotive Multimodal Dataset)

aiMotive dataset is a multimodal dataset for robust autonomous driving with long-range perception. The dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view. The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain and is annotated with 3D bounding boxes with consistent identifiers across frames.

1 PAPER • 1 BENCHMARK

InfiniteRep

InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts.

0 PAPER • NO BENCHMARKS YET

Mouse Embryo Tracking Database

The Mouse Embryo Tracking Database is a dataset for tracking mouse embryos. The dataset contains, for each of the 100 examples: (1) the uncompressed frames, up to the 10th frame after the appearance of the 8th cell; (2) a text file with the trajectories of all the cells, from appearance to division (for cells of generations 1 to 3), where a trajectory is a sequence of pairs (center, radius); (3) a movie file showing the trajectories of the cells.

0 PAPER • NO BENCHMARKS YET

rounD Dataset

rounD Dataset (The Roundabout Drone Dataset - Naturalistic Trajectories of Vehicles and Vulnerable Road Users Recorded at German Roundabouts)

The rounD dataset is a new dataset of naturalistic road user trajectories recorded at German roundabouts. Using a drone, typical limitations of established traffic data collection methods like occlusions are overcome. Traffic was recorded at three different locations. The trajectory for each road user and its type is extracted. Using state-of-the-art computer vision algorithms, the positional error is typically less than 10 centimetres. The dataset is applicable on many tasks such as road user prediction, driver modeling, scenario-based safety validation of automated driving systems or data-driven development of HAD system components.

0 PAPER • NO BENCHMARKS YET

Datasets

32 dataset results for Tracking