Pedestrian-detection
Pedestrian Detection using Non Maximum Suppression
Citing
If you find this code useful in your research, please consider citing the blog:
@misc{sagarpedestrian,
Author = {Abhinav Sagar},
Title = {Pedestrian detection using Non Maximum Suppression algorithm},
Year = {2019},
Journal = {Towards Data Science},
}
Check out the corresponding medium blog post https://towardsdatascience.com/pedestrian-detection-using-non-maximum-suppression-b55b89cefc6.
Environment and tools
- scikit-learn
- scikit-Image
- numpy
- opencv
- nms
- argparse
Non Maximum Suppression
History of Oriented Gradients(HOG) combined with Support Vector Machines(SVM) have been pretty successful for detecting objects in images but the problem with those algorithms is that they detect multiple bounding boxes surrounding the objects in the image. Hence they are not applicable in our case that is detecting pedestrians on crowded roads. Here's where Non maximum suppression(NMS) comes to rescue to better refine the bounding boxes given by detectors. In this algorithm we propose additional penalties to produce more compact bounding boxes and thus become less sensitive to the threshold of NMS. The ideal solution for crowds under their pipelines with greedy NMS is to set a high threshold to preserve highly overlapped objects and predict very compact detection boxes for all instances to reduce false positives.
To execute
python run.py -i sample_images/p2.jpg
Results
References
-
https://www.frontiersin.org/articles/10.3389/fnbot.2018.00064/full
-
https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/leibe-cvpr-05.pdf
-
https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_01126.pdf
License
MIT License
Copyright (c) 2019 Abhinav Sagar
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