Vision-Based Passenger Activity Analysis System in Public Transport and Bus Stop Areas

This study presents the development of a vision system for passenger activity analysis in public transport and bus stop areas. The vision system used people detection and counting algorithm to track the flow of boarding and alighting passengers in a bus stop area. A fuzzy logic controller used inputs from the vision system to determine boarding frequency and alighting frequency for analysis of bus route and dwell time to avoid long queueing that usually cause traffic congestion. People detection and counting result using DS6 dataset (indoor) have 96.81% accuracy with 97.93% precision. People detection and counting result using DS4–1 dataset (outdoor, bus stop area) have 80.39% accuracy with 87.13% precision. Fuzzy simulation results show a boarding frequency of 22 passengers /minute and alighting frequency of 12 passengers /minute. The vision system also analyzed the boarding and alighting of passengers in no loading and unloading areas. This event usually caused traffic bottleneck due to road blockage and long bus queues. In the analysis of DS4–1 (24-hr length) videos, a total of 212 no loading/unloading violations were recorded.

[1]  Mohan M. Trivedi,et al.  Novel concepts and challenges for the next generation of video surveillance systems , 2007, Machine Vision and Applications.

[2]  Hiromitsu Hattori,et al.  Learning From Humans: Agent Modeling With Individual Human Behaviors , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[3]  Kate Saenko,et al.  A combined pose, object, and feature model for action understanding , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Xiaogang Wang,et al.  Cross-scene crowd counting via deep convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Mubarak Shah,et al.  Machine Vision and Applications Understanding Human Behavior from Motion Imagery , 2003 .

[6]  Paolo Giorgini,et al.  Applying social norms to high-fidelity pedestrian and traffic simulations , 2016, 2016 IEEE International Smart Cities Conference (ISC2).

[7]  W. Eric L. Grimson,et al.  Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Yu Qiao,et al.  Object-Scene Convolutional Neural Networks for event recognition in images , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[10]  Mohan M. Trivedi,et al.  A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Iman Gholampour,et al.  Incorporating fully sparse topic models for abnormality detection in traffic videos , 2014, 2014 4th International Conference on Computer and Knowledge Engineering (ICCKE).

[12]  Xiaogang Wang,et al.  Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Argel A. Bandala,et al.  Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling , 2018, J. Adv. Comput. Intell. Intell. Informatics.

[14]  Sanja Fidler,et al.  Visual Semantic Search: Retrieving Videos via Complex Textual Queries , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Sergio A. Velastin,et al.  A Review of Computer Vision Techniques for the Analysis of Urban Traffic , 2011, IEEE Transactions on Intelligent Transportation Systems.

[16]  Amit K. Roy-Chowdhury,et al.  Context-Aware Activity Recognition and Anomaly Detection in Video , 2013, IEEE Journal of Selected Topics in Signal Processing.

[17]  Elmer P. Dadios,et al.  Adaptive driving route of busses along EDSA using Artificial Neural Network (ANN) , 2015, 2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM).

[18]  Argel A. Bandala,et al.  Vehicle detection and tracking using corner feature points and artificial neural networks for a vision-based contactless apprehension system , 2017, 2017 Computing Conference.

[19]  Elmer P. Dadios,et al.  Rerouting of busses along EDSA using Genetic Algorithm , 2014, 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[20]  Xiaogang Wang,et al.  Deeply learned attributes for crowded scene understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Zhenguo Li,et al.  Modeling Scene and Object Contexts for Human Action Retrieval With Few Examples , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Xiaogang Wang,et al.  Scene-Independent Group Profiling in Crowd , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Elmer P. Dadios,et al.  Passenger demand forecast using optical flow passenger counting system for bus dispatch scheduling , 2016, 2016 IEEE Region 10 Conference (TENCON).

[24]  Jean-Marc Odobez,et al.  Topic models for scene analysis and abnormality detection , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[25]  Edwin Sybingco,et al.  Fuzzy logic based vehicular plate character recognition system using image segmentation and scale-invariant feature transform , 2016, 2016 IEEE Region 10 Conference (TENCON).

[26]  Elmer P. Dadios,et al.  Machine vision for traffic violation detection system through genetic algorithm , 2015, 2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM).

[27]  Xiaoqiang Lu,et al.  Scene Recognition by Manifold Regularized Deep Learning Architecture , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Dmitry B. Goldgof,et al.  Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms , 2010, IEEE Transactions on Intelligent Transportation Systems.

[29]  Argel A. Bandala,et al.  Intelligent system architecture for a vision-based contactless apprehension of traffic violations , 2016, 2016 IEEE Region 10 Conference (TENCON).

[30]  Narayanan Vijaykrishnan,et al.  Accelerating neuromorphic vision algorithms for recognition , 2012, DAC Design Automation Conference 2012.