19 datasets found

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 JRMOT dataset is a real-time 3D multi-object tracking dataset used for evaluating the performance of object tracking algorithms.

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

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 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.

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

The H3D dataset for full-surround 3D multi-object detection and tracking in crowded urban scenes.

MOT-Challenge datasets are a benchmark for multi-object tracking.

MOT-Challenge datasets are a benchmark for multi-object tracking.

MOT-Challenge datasets are a benchmark for multi-object tracking.

Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers. Meanwhile, most deep learning models are known to be vulnerable...

More challenging MOTS dataset with higher instance density and crowded scenes.

Multi-object tracking and segmentation (MOTS) dataset, with dense instance segment annotations.

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 dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...