This workshop will bring together the participants of the first Robotic Vision Challenge, a new competition targeting both the computer vision and robotics communities.

The new challenge focuses on probabilistic object detection. The novelty is the probabilistic aspect for detection: A new metric evaluates both the spatial and semantic uncertainty of the object detector and segmentation system. Providing reliable uncertainty information is essential for robotics applications where actions triggered by erroneous but high-confidence perception can lead to catastrophic results.

Program: 17 June 2019, Room 103B

Our workshop will concentrate on the outcomes of the first Probabilistic Object Detection Challenge in the morning session, featuring talks by the four best scoring teams. After that we welcome our invited speakers.

Results of the Probabilistic Object Detection Competition

Place Team Paper
1st Chuan-Wei Wang, Chin-An Cheng, Ching-Ju Cheng, Hou-Ning Hu, Hung-Kuo Chu, Min Sun. National Tsing Hua University. AugPOD: Augmentation-oriented Probabilistic Object Detection
2nd Phil Ammirato, Alexander C. Berg. UNC Chapel Hill. A Mask-RCNN Baseline for Probabilistic Object Detection
3rd Dongxu Li, Chenchen Xu, Yang Liu, Zhenyue Qin. The Australian National University (ANU). Probabilistic Object Detection via Staged Non-Suppression Ensembling
4th Doug Morrison, Anton Milan, Epameinondas Antonakos. Queensland University of Technology (QUT) & Amazon. Uncertainty-aware Instance Segmentation using Dropout Sampling

Detailed results of the top four participating teams, as evaluated by the PDQ measure:

Place PDQ Score Average Overall Quality Average Spatial Quality Average Label Quality True Positives False Positives False Negatives
1st 22.563 0.605 0.454 1.000 152967.0 113620.0 143400.0
2nd 21.432 0.662 0.503 0.999 125332.0 90853.0 171035.0
3rd 20.019 0.655 0.497 1.000 118678.0 91735.0 177689.0
4th 14.650 0.578 0.479 0.853 87438.0 48327.0 208929.0

Participate in the Competition

The competition server is now closed, but will re-open around September 2019 for the second round of this challenge. It will be presented at our workshop at IROS in November 2019.

To participate in the competition, and for more information around the data and submission format, please go to our Codalab page.

Our first challenge requires participants to detect objects in video data (from high-fidelity simulation). As a novelty, our evaluation metric rewards accurate estimates of spatial and semantic uncertainty using probabilistic bounding boxes. We developed a new probability-based detection quality (PDQ) evaluation measure for this challenge, please see the arxiv paper for more details.

Submissions must be accompanied by a 3-6 page paper explaining the method and external data used. Please use the CVPR paper format (no need to keep it double-blind) and upload your paper through our submission page on (CMT). Top performing submissions from the challenge will be invited to present their methods at the workshop. A total of $5000 AUD prize money will be rewarded to the competitors, subject to the rules explained on the Codalab page.

Important Dates


The Robotic Vision Challenges organisers are with the Australian Centre for Robotic Vision and Google AI.

Niko Sünderhauf
Queensland University of Technology
Feras Dayoub
Queensland University of Technology
Anelia Angelova
Google Brain
David Hall
Queensland University of Technology
John Skinner
Queensland University of Technology
Haoyang Zhang
Queensland University of Technology
Gustavo Carneiro
University of Adelaide