Parking Slot Detection Opencv

The parking-slot detection module takes the surround-view image as the input, detects the parking-slots, and finally sends their physical positions with respect to the vehicle-centered coordinate system to the decision module for further process. Representative work on vision-based parking-slot detection will be reviewed as follows. OpenCV is an extensive open source library (available in python, Java, and C) that’s used for image analysis and is pretty neat. The lofty goal for my OpenCV experiment was to take any static image or video of a parking lot and be able to automatically detect whenever a parking space was available or occupied.

Dataset Download

You can download CNRPark+EXT using the following links:

Parking Slot Detection Opencv

• CNRPark+EXT.csv (18.1 MB)

  CSV collecting metadata for each patch of both CNRPark and CNR-EXT datasets

• CNRPark-Patches-150x150.zip (36.6 MB)

  segmented images (patches) of parking spaces belonging to the CNRPark preliminary subset.
  Files follow this organization: <CAMERA>/<CLASS>/YYYYMMDD_HHMM_<SLOT_ID>.jpg, where:

  • <CAMERA> can be A or B,
  • <CLASS> can be free or busy,
  • YYYYMMDD_HHMM is the zero-padded 24-hour capture datetime,
  • <SLOT_ID> is a local ID given to the slot for that particular camera

  E.g:A/busy/20150703_1425_32.jpg

• CNR-EXT-Patches-150x150.zip (449.5 MB)

  segmented images (patches) of parking spaces belonging to the CNR-EXT subset.
  Files follow this organization:
  PATCHES/<WEATHER>/<CAPTURE_DATE>/camera<CAM_ID>/<W_ID>_<CAPTURE_DATE>_<CAPTURE_TIME>_C0<CAM_ID>_<SLOT_ID>.jpg,
  where:

  • <WEATHER> can be SUNNY, OVERCAST or RAINY,
  • <CAPTURE_DATE> is the zero-padded YYYY-MM-DD formatted capture date,
  • <CAM_ID> is the number of the camera, ranging 1-9,
  • <W_ID> is a weather identifier, that can be S, O or R,
  • <CAPTURE_TIME> is the zero-padded 24-hour HH.MM formatted capture time,
  • <SLOT_ID> is a global ID given to the monitored slot; this can be used to uniquely identify a slot in the CNR-EXT dataset.

  E.g:PATCHES/SUNNY/2015-11-22/camera6/S_2015-11-22_09.47_C06_205.jpg

  The LABELS folder contains a list file for each split of the dataset used in our experiments. Each line in list files follow this format: <IMAGE_PATH> <LABEL>, where:

  • <IMAGE_PATH> is the path to a slot image,
  • <LABEL> is 0 for free, 1 for busy.

• CNR-EXT_FULL_IMAGE_1000x750.tar (1.1 GB)

  full frames of the cameras belonging to the CNR-EXT subset. Images have been downsampled from 2592x1944 to 1000x750 due to privacy issues.
  Files follow this organization:
  FULL_IMAGE_1000x750/<WEATHER>/<CAPTURE_DATE>/camera<CAM_ID>/<CAPTURE_DATE>_<CAPTURE_TIME>.jpg,
  where:

  • <WEATHER> can be SUNNY, OVERCAST or RAINY,
  • <CAPTURE_DATE> is the zero-padded YYYY-MM-DD formatted capture date,
  • <CAM_ID> is the number of the camera, ranging 1-9,
  • <CAPTURE_TIME> is the zero-padded 24-hour HHMM formatted capture time.

  The archive contains also 9 CSV files (one per camera) containing the bounding boxes of each parking space with which patches have been segmented. Pixel coordinates of the bouding boxes refer to the 2592x1944 version of the image and need to be rescaled to match the 1000x750 version.

• splits.zip (27.2 MB)

  all the splits used in our experiments. Those splits combine our datasets and also third-party datasets (such as PKLot).