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a5329f0 May 22, 2018
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Road Object Detection

We use the following 3 metrics to evaluate the performance of detection:

Average Precision (AP):

Metric
APIoU=.75 % AP at IoU=.75

Submission format

The entire result struct array is stored as a single JSON file (save via gason in Matlab or json.dump in Python).

[  
   {  
      "name": str,
      "timestamp": 1000,
      "category": str,
      "bbox": [x1, y1, x2, y2],
      "score": float
   }
]

Box coordinates are integers measured from the top left image corner (and are 0-indexed). [x1, y1] is the top left corner of the bounding box and [x2, y2] the lower right. name is the video name that the frame is extracted from. It composes of two 8-character identifiers connected '-', such as c993615f-350c682c. Candidates for category are ['bus', 'traffic light', 'traffic sign', 'person', 'bike', 'truck', 'motor', 'car', 'train', 'rider']. In the current data, all the image timestamps are 1000.

Segmentation

Both drivable area and semantic segmentation follow the same evaluation metric.

Following the practice of Cityscapes challenge, we calculate the intersection-over-union metric from PASCAL VOC across the whole test set, IoU=true positive/true positive+false positive+false negative.

Result files with filename "XXX*.png" where XXX is the corresponding name of test video (19-character identifier). The image size of results should be equal to the input image size. The encoding of labels should still be train_id, thus car should be 13.