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Beyond estimating discrete directions of walk: a fuzzy approach

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Abstract

Direction of walk of a pedestrian is vital information in various applications like visual surveillance, traffic monitoring and control, assisted living systems and automated car assistance system. Existing methods of direction of walk estimation exploit inter-frame and intra-frame features of a pedestrian frame sequence to classify the motion among predefined discrete direction classes. However, in order to achieve a robust method to estimate direction of walk, a strong analogy to justify an evident stationary or motion pattern as potential feature for direction estimation is essential. Discrete results of walk direction are famously used as it can be estimated in less time and are preferable for less precision sensitive scenario. However, the intra-frame feature that yields per-frame orientation is underutilized when the walk directions are subjected to discrete classes and hence there is a stringent need to go beyond discrete levels of direction and comment on specific walk direction angles at the same time maintaining a strong analogy to justify the potential of proposed features for direction of walk estimation. With this motivation, the article proposes a type-1 fuzzy approach over apposite inter-frame as well as intra-frame locomotion feature of pedestrian to yield precise direction of walk in terms of fuzzy directions beyond discrete levels of pedestrian walk directions. The method identifies eight directions as potential membership functions. Identified features are subjected to rule-based table for identification and removal of noisy orientation results, decision on membership function and their membership grade generation. The defuzzified result yields crisp direction of walk estimates beyond discrete levels of direction. The enhanced direction of walk estimation results is evident from qualitative comparison from existing research works and is also supported by the simulation performed over different datasets. The proposed method achieves 98.33%, 98.79%, and 100% Rank-2 balanced accuracy while applied on CASIA Dataset A, B, and NITR Conscious Walk Dataset respectively, which clearly points out its better performance than state of the art.

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Abbreviations

CASIA Dataset:

Institute of Automation Chinese Academy of Sciences Dataset

DR:

Dead Reckoning

GLMPC:

Global Local Motion Pattern Classification

GPS:

Global Positioning System

HMM:

Hidden Markov Model

HoG:

Histogram of Gradient

HoF:

Histogram of Flow

kNN:

k-Nearest Neighbors

LBP:

Local Binary Pattern

MEMS-IMU:

Micro-Electro-Mechanical System Inertial Measurement Unit

NITRCWD:

National Institute of Technology Rourkela Conscious Walk Dataset

SVM:

Support Vector Machine

LS-SVM:

Least Square Support Vector Machine

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Acknowledgements

The research presented in this article is funded by Grant No. 12(5)/2012-ESD by Department of Electronics and Information Technology, Government of India. The work described in the article is extended from R. Raman, P. K. Sa, S. Bakshi, and B. Majhi, Kinesiology-inspired estimation of pedestrian walk direction for smart surveillance, Future Generation Computer Systems (2017) DOI: 10.1016/j.future.2017.10.033.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, 769 008, Odisha, India

    Rahul Raman, Pankaj Kumar Sa, Banshidhar Majhi & Sambit Bakshi

  2. Department of Computer Science and LIASD research Laboratory, University of Paris 8, Saint-Denis, France

    Larbi Boubchir

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  1. Rahul Raman
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Correspondence to Sambit Bakshi.

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Raman, R., Boubchir, L., Sa, P.K. et al. Beyond estimating discrete directions of walk: a fuzzy approach. Machine Vision and Applications 30, 901–917 (2019). https://doi.org/10.1007/s00138-018-0939-6

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