Accurate pedestrian localization in overhead depth images via Height-Augmented HOG

Werner Kroneman, Alessandro Corbetta, Federico Toschi

Abstract


We tackle the challenge of reliably and automatically localizing pedestrians in real-life conditions through overhead depth imaging at unprecedented high-density conditions. Leveraging upon a combination of Histogram of Oriented Gradients-like feature descriptors, neural networks, data augmentation and custom data annotation strategies, this work contributes a robust and scalable machine learning-based localization algorithm, which delivers near-human localization performance in real-time, even with local pedestrian density of about 3 ped/m2, a case in which most stateof- the art algorithms degrade significantly in performance.

Keywords


real-life pedestrian dynamics measurements; depth-based localization; machine learning; augmented HOG features; high-density localization

Full Text:

PDF

References


A. Corbetta, C. Lee, R. Benzi, A. Muntean and F. Toschi, “Fluctuations around mean walking

behaviors in diluted pedestrian flows,” in Physical Review E, vol. 95, no. 3, 032316, 2017.

A. Corbetta, L. Bruno, A. Muntean and F. Toschi, “High Statistics Measurements of Pedestrian

Dynamics,” Transportation Research Procedia, vol. 2, pp. 96–104, 2014.

Microsoft Corporation, “Kinect for Xbox 360,” Redmond, WA, USA.

A. Corbetta, J. Meeusen, C. Lee and F. Toschi, “Continuous measurements of real-life

bidirectional pedestrian flows on a wide walkway,” in Proceedings of Pedestrian and Evacuation

Dynamics, pp. 18–24, 2016.

D. Brscic, T. Kanda, T. Ikeda and T. Miyashita, “Person Tracking in Large Public Spaces Using

-D Range Sensors,” in IEEE Transactions on Human-Machine Systems, vol. 43, no. 6, pp. 522–

, 2013.

S. Seer, N. Brändle and C. Ratti, “Kinects and human kinetics: A new approach for studying

pedestrian behavior,” in Transportation Research Part C: Emerging Technologies, vol. 48, pp.

–228, 2014.

A. Corbetta, W. Kroneman, M. Donners, A. Haans, P. Ross, M. Trouwborst, S. vd Wijdeven, M.

Hultermans, D. Sekulowski, F. vd Heijden, S. Mentink and F. Toschi, “A large-scale real-life

crowd steering experiment via arrow-like stimuli,” in Pedestrian and Evacuation Dynamics, 2018

(accepted).

N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in IEEE

Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05), 2005, pp.

–893

A. Corbetta, “Multiscale crowd dynamics: physical analysis, modeling and applications,” Ph. D.

thesis, Eindhoven University of Technology, 2016.

J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real-Time

Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition

(CVPR), 2016, pp. 779–788.

A. Corbetta, V. Menkovski and F. Toschi, “Weakly supervised training of deep convolutional

neural networks for overhead pedestrian localization in depth fields,” in 14th IEEE International

Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017, pp 1-6.

R. Collobert and S. Bengio, “Links between perceptrons, MLPs and SVMs,” in Proceedings of

the Twenty-first international conference on Machine learning - ICML 04, 2004.

Q. Xia, H.-D. Zhu, Y. Gan and L. Shang, “Plant Leaf Recognition Using Histograms of

Oriented Gradients,” in Intelligent Computing Methodologies Lecture Notes in Computer Science,

pp. 369–374, 2014.

D. Ciresan, U. Meier and J. Schmidhuber, “Multi-column deep neural networks for image

classification,” Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern

Recognition, 2012, pp. 3642-3649.

L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using

Deep Learning,” arXiv:1712.04621, 2017.

K.-K. Sung, “Learning and example selection for object and pattern detection,” Ph. D. thesis,

Massachusetts Institute of Technology, 1996.

M. Abadi, et al, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,”

Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation

(OSDI’16), 2016, pp 265-283.

F. Chollet, et al, “Keras,” 2015. Available: https://keras.io.




DOI: http://dx.doi.org/10.17815/CD.2020.30

Copyright (c) 2020 Werner Kroneman, Alessandro Corbetta, Federico Toschi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.