34 datasets found

The Global Data Association for Multi-Object Tracking using Network Flows dataset is used to evaluate the performance of multi-object tracking algorithms.

The proposed Tracking-by-Animation (TBA) framework achieves unsupervised end-to-end learning of MOT tasks.

Object detectors are vital to many modern computer vision applications. However, even state-of-the-art object detectors exhibit inconsistent behavior when the input undergoes...

Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based...

The Cars dataset is a multi-object multi-camera network application.

The dataset used for the MOT20 challenge, which consists of 8 different sequences depicting very crowded challenging scenes.

MOT16 is a benchmark for multi-object tracking, consisting of 16 video sequences with 1000 frames each.

The BDD100K MOTS dataset is a subset of the BDD100K dataset, containing 154 videos with annotation for training and validation, and 37 videos for testing.

The MOTS20 dataset is used for multi-object tracking and segmentation.

The MOT16, MOT17, and MOT20 datasets are used for evaluating the proposed One More Check (OMC) tracker.

The CLEAR MOT metrics are used to evaluate the performance of multi-object tracking algorithms.

Object permanence is the concept that objects in a physical world continue to exist despite the observers inability to sense them. This can result in temporally unstable...

The MOTS dataset is a variant of the MOT dataset, containing 4 training sequences and 4 test sequences.

The MOT16 and MOT17 datasets are used for evaluating the performance of multi-object tracking algorithms.

The TraDeS tracker is a novel online joint detection and tracking model that exploits tracking clues to assist detection and in return benefits tracking.

Multi-Camera Multi-Object Tracking (MC-MOT) dataset for reconfigurable spatial-temporal graph model

Multi-Camera Multi-Object Tracking (MC-MOT) dataset for reconfigurable spatial-temporal graph model

Multi-Camera Multi-Object Tracking (MC-MOT) dataset for reconfigurable spatial-temporal graph model

A synthetic dataset for multi-object tracking, providing ground truth annotations for invisible objects.

MOT17Det is a dataset for people detection from the Multi-Object Tracking (MOT) Challenge 1. It comprises of 14 videos under different lighting, view, and weather conditions.