Version: 2.0

OS: Linux (Ubuntu 14.04 is recommended)

• OpenCV2
• Python2

Follow the link to get multiple Haar classifier cascades.

• obj_detect.py: The function that you will use
• support/ The cpp code that supports the function of "obj_detect.py"
• cascade/ Trained classifiers. Include AT&T, Chase, Macy's, Mobil, Subway, Walmart, and banana (Taco Bell and Starbucks will come soon...).
• history/ Previous version of logo detection code
• images/ Images w/ or w/o logos
• tool/ Includes a script that can help you find the best MARGIN and THRESHOLD to have acceptable FPR and FNR. There is also an analysis result text file generated by me.
  • log/ contains detection analysis log with the margin from 0 to 25
  • plot_round_num/ has the images that show the distribution of round number with different margins

Given image path and logo name, the "get_result" function in "obj_detect.py" will return the x-coordinate of the detected logo.

Put "obj_detect.py" in the same path with the "support" folder. Then import "obj_detect.py" into your code and call "get_result" function.

Sample: In python, use:
- $ res = get_result("walmart", "images/walmart/walmart.jpg", 1)

• Input parameters:

  • "walmart" is the name of logo that you want to detect
  • "images/walmart/walmart.jpg" is the path of the image to process
  • 1 means show the detection result; 0 means do not show the result (Displaying the result will block the get_result function and need PRESS_ANY_KEY to continue)

• Returned string:

  • If the program returns correctly, "res" will be in the format like "logo_left / logo_right / image_width / min_neighbor / conf_score".
  • For "logo_left" and "logo_right", -1 means more than one object found; 0 means no object;
  • If something goes wrong, "res" will be "ERROR"

In object detection, there is a score called "MIN_NEIGHBOR" which represents the similarity between the detected area and sample object. Moreover, I applied recursive image cropping to get the number of "cropping round", which can also be used to know if there is a real logo or not. In most cases, real logo will have higher round number while the false positive has less.

Threshold of conf_score To remove false positive cases, we need to ignore the results with confidence scores smaller than a certain threshold. The relation between the threshold, FPR, and FNR is partially listed below. You can select a threshold according to your application scenario.

• Threshold / FPR(%) / FNR(%)
• 10 / 15.9 / 13.0
• 11 / 15.9 / 14.4
• 12 / 15.9 / 14.4
• 13 / 14.4 / 14.4
• 14 / 13.0 / 14.4
• 15 / 11.5 / 15.9
• 16 / 11.5 / 15.9
• 17 / 10.1 / 18.8
• 18 / 8.6 / 18.8
• 19 / 8.6 / 20.2
• 20 / 5.7 / 21.7 (The data is tested among 64 images in 2015 May, new result will be updated shortly)

• Chase cascade updated (with the trainining set of streetview as background)

Please cite this paper in your publications if it helps your research

@inproceedings{Hu:2016:AAL:2971648.2971674,
 author = {Hu, Yitao and Liu, Xiaochen and Nath, Suman and Govindan, Ramesh},
 title = {ALPS: Accurate Landmark Positioning at City Scales},
 booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
 series = {UbiComp '16},
 year = {2016},
 isbn = {978-1-4503-4461-6},
 location = {Heidelberg, Germany},
 pages = {1147--1158},
 numpages = {12},
 url = {http://doi.acm.org/10.1145/2971648.2971674},
 doi = {10.1145/2971648.2971674},
 acmid = {2971674},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {context-aware computing, landmark localization system, machine/deep learning},
}