Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in thi
358 PAPERS • 15 BENCHMARKS
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.
175 PAPERS • 8 BENCHMARKS
Consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system.
50 PAPERS • 1 BENCHMARK
The Multi-Object and Segmentation (MOTS) benchmark [2] consists of 21 training sequences and 29 test sequences. It is based on the KITTI Tracking Evaluation 2012 and extends the annotations to the Multi-Object and Segmentation (MOTS) task. To this end, we added dense pixel-wise segmentation labels for every object. We evaluate submitted results using the metrics HOTA, CLEAR MOT, and MT/PT/ML. We rank methods by HOTA [1]. Our development kit and GitHub evaluation code provide details about the data format as well as utility functions for reading and writing the label files. (adapted for the segmentation case). Evaluation is performed using the code from the TrackEval repository.
26 PAPERS • 1 BENCHMARK
A new large-scale dataset for understanding human motions, poses, and actions in a variety of realistic events, especially crowd & complex events. It contains a record number of poses (>1M), the largest number of action labels (>56k) for complex events, and one of the largest number of trajectories lasting for long terms (with average trajectory length >480). Besides, an online evaluation server is built for researchers to evaluate their approaches.
18 PAPERS • 1 BENCHMARK
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.
17 PAPERS • 2 BENCHMARKS
PathTrack is a dataset for person tracking which contains more than 15,000 person trajectories in 720 sequences.
8 PAPERS • NO BENCHMARKS YET
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.
5 PAPERS • 1 BENCHMARK
Caltech Fish Counting Dataset (CFC) is a large-scale dataset for detecting, tracking, and counting fish in sonar videos. This dataset contains over 1,500 videos sourced from seven different sonar cameras.
2 PAPERS • NO BENCHMARKS YET