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KITTI Object Detection Benchmark
The KITTI Object Detection Benchmark consists of 7,481 training images and 7,518 testing images, with 3D LiDAR point clouds and camera images. -
2DMOT15 and 2DMOT16 Datasets for Multi-Object Tracking
The 2DMOT15 and 2DMOT16 datasets are used to evaluate the performance of the proposed online MOT algorithm. -
H3D Dataset
The H3D dataset for full-surround 3D multi-object detection and tracking in crowded urban scenes. -
MOT20: A Benchmark for Multi-Object Tracking
MOT-Challenge datasets are a benchmark for multi-object tracking. -
MOT16: A Benchmark for Multi-Object Tracking
MOT-Challenge datasets are a benchmark for multi-object tracking. -
MOT-Challenge datasets
MOT-Challenge datasets are a benchmark for multi-object tracking. -
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking T...
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 KITTI
Virtual worlds as proxy for multi-object tracking analysis -
APOLLO MOTS
More challenging MOTS dataset with higher instance density and crowded scenes. -
KITTI MOTS
Multi-object tracking and segmentation (MOTS) dataset, with dense instance segment annotations. -
KITTI dataset
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...
