34 datasets found

The dataset used for tracking multiple people across multiple cameras is a large-scale multi-camera tracking dataset, DukeMTMC.

The KITTI Object Detection Benchmark consists of 7,481 training images and 7,518 testing images, with 3D LiDAR point clouds and camera images.

The 2DMOT15 and 2DMOT16 datasets are used to evaluate the performance of the proposed online MOT algorithm.

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...

Virtual worlds as proxy for multi-object tracking analysis

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 GMOT-40 dataset is a benchmark for generic multi-object tracking, containing 40 videos with 10 different object categories.

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...