Parking Slot Detection Opencv

1. Parking Slot Detection Opencv Machine Learning
2. Parking Slot Detection Opencv Software
3. Opencv Object Detection
4. Opencv Pattern Detection

Methods in 26-34 detect parking slot markings in a full-automatic manner. 26 proposed a method that recognizes parking slot markings using a neural network-based color segmentation. 27 detected parking slots by finding parallel line pairs using a specialized filter and Hough transform. Perform bitwise xor between above mask and first threshold image, this will give you mask for each parking slot. Using this mask you can analyse the background colour for parking slot, can decide whether it is occupied or not.

Parking Slot Detection Opencv Machine Learning

Dataset Download

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. Asks the user to enter a numerical value to set the lower threshold for our Canny Edge Detector (by means of a Trackbar). Applies the Canny Detector and generates a mask (bright lines representing the edges on a black background). Applies the mask obtained on the original image and display it in a window. Explanation (C code).

Parking Slot Detection Opencv Software

1. Parking Slot Detection Opencv Machine Learning
2. Parking Slot Detection Opencv Software
3. Opencv Object Detection
4. Opencv Pattern Detection

Methods in 26-34 detect parking slot markings in a full-automatic manner. 26 proposed a method that recognizes parking slot markings using a neural network-based color segmentation. 27 detected parking slots by finding parallel line pairs using a specialized filter and Hough transform. Perform bitwise xor between above mask and first threshold image, this will give you mask for each parking slot. Using this mask you can analyse the background colour for parking slot, can decide whether it is occupied or not.

Parking Slot Detection Opencv Machine Learning

Dataset Download

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. Asks the user to enter a numerical value to set the lower threshold for our Canny Edge Detector (by means of a Trackbar). Applies the Canny Detector and generates a mask (bright lines representing the edges on a black background). Applies the mask obtained on the original image and display it in a window. Explanation (C code).

Parking Slot Detection Opencv Software

Opencv Object Detection

You can download CNRPark+EXT using the following links:

Opencv Pattern Detection

• 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: //YYYYMMDD_HHMM_.jpg, where:

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

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

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• 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///camera/___C0_.jpg,
  where:

  • can be SUNNY, OVERCAST or RAINY,
  • is the zero-padded YYYY-MM-DD formatted capture date,
  • is the number of the camera, ranging 1-9,
  • is a weather identifier, that can be S, O or R,
  • is the zero-padded 24-hour HH.MM formatted capture time,
  • 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: , where:

  • is the path to a slot image,
  • 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///camera/_.jpg,
  where:

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  • can be SUNNY, OVERCAST or RAINY,
  • is the zero-padded YYYY-MM-DD formatted capture date,
  • is the number of the camera, ranging 1-9,
  • 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).