3D Object Detection From Stereo Images

KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist. Source: A Review on Deep Learning Techniques Applied to Semantic Segmentation Image Source: http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d

The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals. Typically, datasets for animal-specific vision tasks are created using open-source video material. This might be effective for an initial start, but these methods are not deployment ready for the behavior community. Therefore, we designed a semi-automated method for biologists to create well-curated datasets at a large scale for the ML and Vision community. 3D-POP is the first dataset with 3D ground truth for multi-animal, multi-view tracking problems. Highlight: The dataset is captured with the intention of using it for various vision problems and with different levels of complexity (no of cameras, no of individuals) Video explanation: Link to YouTube video Video teaser: Link to YouTube video ## Dataset Features: ### Marker-based videos: - 6 hours+ of annotations of 18 individuals (groups of 1, 2, 5, 10). - Bounding box - Trajectories (2D and 3D) - Posture (2D and 3D) with 9 key points - Identities - Total of 57 sequences (4K) with 4 views. - Dataset customization* (Users can modify the dataset and add key points to the dataset) ### Markerless: - 1Hr+ videos of 18 individuals in groups of 1, 2, 5, 11. The birds have no markers. This data is provided as test cases and unsupervised approaches. ## Problems: ### 2D domain: - Position, Posture of birds (different group sizes n = 1, 2, 5, 10) with Single/Multiview. - Tracking with single - multiview ### 3D domain: - Position, Posture of birds (different group sizes n = 1, 2, 5, 10) with Single/Multiview. - Tracking with single - multiview ### Fine-grained recognition: - Identity tracking with ground truth. ### Unsupervised learning: - 2D or 3D posture problems ## Idea: The dataset is created with a motion capture system, using the 6-DOF tracking ability. Assumptions are that head and body act as rigid bodies when birds walk and forage (proved with experiment). Therefore, we get the 3D position of key points by tracking head/body orientation.

A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments.

3D-ZeF dataset consists of eight sequences with a duration between 15-120 seconds and 1-10 free moving zebrafish. The videos have been annotated with a total of 86,400 points and bounding boxes. Source: 3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset