* Instructions can be found
CalmCar, future mobility part supplier, specialized in deep learning-based embedded vision products and data services. CalmCar core product of multi-camera active surround view perception system is developed based on automotive grade computing platform, it enables applications of smart parking, L2+ autonomous driving and crowdsourced mapping.
Multi-target multi-camera (MTMC) tracking systems can automatically track multiple vehicles using an array of cameras. In this challenge, participants are required to design robust MTMC tracking algorithms, which are targeted at vehicles, where the same vehicles captured by different cameras possess the same tracking IDs. The competitors will have access to four large-scale training datasets, each of which includes around 1200 annotated RGB images, where the labels cover the types of vehicles, tracking IDs and 2D bounding boxes. Identification precision (IDP) and identification recall (IDR) will be used as metrics to evaluate the performance of the implemented algorithms. The competitors are required to submit their pretrained models as well as the corresponding docker image files via the CMT submission system for algorithm evaluation (in terms of both speed and accuracy).
The winner of the competition will receive a monetary prize (US$5000) and will give a keynote presentation at the workshop.
* Instructions can be found
HKUST is commonly regarded as one of the fastest-growing universities in the world. In 2019, the university was ranked seventh in Asia by QS and third by The Times, and around top 40 internationally. It was ranked 27th in the world and second in Hong Kong by QS 2021.
UDI is committed to making autonomous systems everywhere. UDI focuses on creating safe, stable, and mass-produced autonomous driving vehicles, providing integrated, efficient, and reproducible intelligent logistics solutions. UDI is leading the development of logistics automation in the era of Industry 4.0.
Deep neural networks excel at learning from large amounts of data but they can be inefficient when it comes to generalizing and applying learned knowledge to new datasets or environments. In this competition, participants need to develop an unsupervised domain adaptation (UDA) framework which can allow a model trained on a large synthetic dataset to generalize to real-world imagery. The tasks in this competition include: 1) UDA for monocular depth prediction and 2) UDA for semantic driving-scene segmentation. The competitors will have access to Ready to Drive (R2D) dataset, which is a large-scale synthetic driving scene dataset collected under different weather/illumination conditions using the Carla Simulator. In addition, competitors will also have access to a small amount of real-world data. The mean absolute value of the relative (mAbsRel) error and the mean intersection over union (mIoU) score will be used as metrics to evaluate the performance of UDA for monocular depth prediction and UDA for semantic driving scene segmentation, respectively. The competitors will be required to submit their pretrained models and docker image files via the CMT submission system.
The winner of the competition will give a keynote presentation at the workshop.
Researchers of top-ranked object detection algorithms submitted to the KITTI Object Detection Benchmarks will have the opportunity to present their work at the 1st AVVision workshop, subject to space availability and approval by the workshop organizers. It should be noted that only the algorithms submitted before 12/20/2020 are eligible for presentation at the 1st AVVision workshop.