Online Multi-Object Tracking

The Multiple Object Tracking 17 (MOT17) dataset is a dataset for multiple object tracking. Similar to its previous version MOT16, this challenge contains seven different indoor and outdoor scenes of public places with pedestrians as the objects of interest. A video for each scene is divided into two clips, one for training and the other for testing. The dataset provides detections of objects in the video frames with three detectors, namely SDP, Faster-RCNN and DPM. The challenge accepts both on-line and off-line tracking approaches, where the latter are allowed to use the future video frames to predict tracks. Source: Deep Affinity Network for Multiple Object Tracking Image Source: https://www.researchgate.net/figure/Visualization-of-selected-sequences-from-the-MOT17-benchmark-dataset_fig4_337133502

The MOTChallenge datasets are designed for the task of multiple object tracking. There are several variants of the dataset released each year, such as MOT15, MOT17, MOT20. Source: MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

The MOT16 dataset is a dataset for multiple object tracking. It a collection of existing and new data (part of the sources are from and ), containing 14 challenging real-world videos of both static scenes and moving scenes, 7 for training and 7 for testing. It is a large-scale dataset, composed of totally 110407 bounding boxes in training set and 182326 bounding boxes in test set. All video sequences are annotated under strict standards, their ground-truths are highly accurate, making the evaluation meaningful. Source: SOT for MOT Image Source: https://www.researchgate.net/figure/Sample-results-on-the-sequence-MOT16-07-encoded-as-in-the-previous-figure-Table-1_fig3_309641746

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. Source: FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking Image Source: https://www.researchgate.net/figure/Exemplary-qualitative-tracking-results-for-the-MOT15-benchmark-dataset-a-d-are-from-a_fig1_340328377

Motivation Multi-object tracking (MOT) is a fundamental task in computer vision, aiming to estimate objects (e.g., pedestrians and vehicles) bounding boxes and identities in video sequences. Prevailing human-tracking MOT datasets mainly focus on pedestrians in crowded street scenes (e.g., MOT17/20) or dancers in static scenes (DanceTrack). In spite of the increasing demands for sports analysis, there is a lack of multi-object tracking datasets for a variety of sports scenes, where the background is complicated, players possess rapid motion and the camera lens moves fast. To this purpose, we propose a large-scale multi-object tracking dataset named SportsMOT, consisting of 240 video clips from 3 categories (i.e., basketball, football and volleyball). The objective is to only track players on the playground (i.e., except for a number of spectators, referees and coaches) in various sports scenes. We expect SportsMOT to encourage the community to concentrate more on the complicated sports scenes. ## Characteristics - Large scale - Fine Annotations - Player id consistency - No shot change - High and fixed resolution(1080P) - ... ## Focus - Diverse sports scenes - Complex motion patterns - Challenging re-id ## Download ### Examples You can download the example for SportsMOT. - OneDrive - Baidu Netdisk, password: 4dnw ### Official Dataset Please Sign up in codalab, and participate in our competition. Download links are available in Participate/Get Data. ## News - SportsMOT is used for DeeperAction@ECCV-2022. - Refer to github repo: MCG-NJU/SportsMOT for the latest info.

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. We expect the availability of such large-scale multi-camera multiple people tracking dataset will encourage more participants in this research topic. This dataset is also valuable for the evaluation of other tasks, such as multi-view people detection and monocular multiple people tracking.

We collected 32 videos that record bee colony activity from different periods on several sunny days. The total size of the dataset is 3,562 frames and 43,169 annotations.

CholecTrack20 is a surgical video dataset focusing on laparoscopic cholecystectomy and designed for surgical tool tracking, featuring 20 annotated videos. The dataset includes detailed labels for multi-class multi-tool tracking, offering trajectories for tool visibility within the camera scope, intracorporeal movement within the patient's body, and the life-long intraoperative trajectory of each tool. Annotations cover spatial coordinates, tool class, operator identity, phase, visual conditions (occlusion, bleeding, smoke), and more for tools like grasper, bipolar, hook, scissors, clipper, irrigator, and specimen bag, with annotations provided at 1 frame per second across 35K frames and 65K instance tool labels. The dataset uses official splits, allocating 10 videos for training, 2 for validation, and 8 for testing. Chinedu Nwoye

PersonPath22 is a large-scale multi-person tracking dataset containing 236 videos captured mostly from static-mounted cameras, collected from sources where we were given the rights to redistribute the content and participants have given explicit consent. Each video has ground-truth annotations including both bounding boxes and tracklet-ids for all the persons in each frame.

The Oxford Town Center dataset is a 5-minute video with 7500 frames annotated, which is divided into 6500 for training and 1000 for testing data for pedestrian detection. The data was recorded from a CCTV camera in Oxford for research and development into activity and face recognition. Source: LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning Image Source: https://megapixels.cc/oxford_town_centre/