A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes

• There are five benchmark datasets tested on the proposed methodology:

1. Berlin Halenseestrasse
2. Berlin Kudamm
3. Berlin A100
4. Garden Point
5. Syhthesized Nordland

If you use these datasets and code, please cite the following publication:

@article{khaliq2018holistic,
  	author={Khaliq, Ahmad and Ehsan, Shoaib and Chen, Zetao and Milford, Michael and McDonald-Maier, Klaus},
	journal={IEEE Transactions on Robotics},
	title={A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes},
	year={2019},
	volume={},
	number={},
	pages={1-9},
	keywords={Convolutional neural network (CNN);feature encoding;robot localization;vector of locally aggregated descriptors (VLADs);visual place recognition (VPR)},
	doi={10.1109/TRO.2019.2956352},
	ISSN={1941-0468},
	month={},
}

• Each dataset contains two traverses of the same route under different viewpoints and conditions

  • A "Result" folder in each dataset contains the matched and unmatched image files using the proposed approach
    • For each test image, the retrived image is correct if its name is written in green color else red color shows it is treated as unmatched
    • In some unmatched scenarios, we observed that the retrieved image is quite similar/closer to the test image but due to the ground truth priorities, our algorithm kept those unmatched which reflects in the AUC-PR curve.
  • Pickle files are there for two settings i.e. N= 200 Regions with V =128 clusters and N =400 Regions with V =256 clusters
    • Each file is a dictionary containing keys as test images names and values contain the retrieval information i.e. scores , retrieved image names etc
      • Each Value is a list which contains the name of ground truth image, predicted label, prediction score and the retrieved image name

• There is another folder "Vocabulary", containing dataset "2.6K", employed for making regional dictionaries

  • Two pickle files are there, one with N= 400 regions clustered into V= 64,128,256 regions, where the other file contains N= 100,200,300 regions clustered again into V= 64,128,256 regions each. Each file is again a nested dictionary with nested keys as Region (N) and Cluster (V).

• A python script "produceResults.py" can generate the ground truth for Berlin datasets and all the datasets' results i.e. AUC-PR and retrieved images for both the configurations using the Pickle files. The user just needs to defined the "datasetIndex" and "dir" parameter.

• A python script "Region-VLAD.py" is the main implementation file. The user needs to externally download the "alexnet_places365.caffemodel" from https://github.com/CSAILVision/places365, set the paths and have pythonic libararies including caffe installed to run the script. The script loads the model, pass down the sample test and reference image and returns the matching score.

Configuration 1: N = 200, V=128 (AUC-PR Results)

1. Berlin Halenseestrasse: 0.754
2. Berlin Kudamm: 0.298
3. Berlin A100: 0.7381
4. Garden Point: 0.6540
5. Synthesized Nordland: 0.539

Configuration 2: N = 400, V=256 (AUC-PR Results)

1. Berlin Halenseestrasse: 0.808
2. Berlin Kudamm: 0.395
3. Berlin A100: 0.7117
4. Garden Point: 0.7225
5. Synthesized Nordland: 0.547