Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based Approach
The paper proposes a novel approach for direction estimation of a moving pedestrian as perceived in a 2-D coordinate of field camera. The proposed direction estimation method is intended for pedestrian monitoring in traffic control systems. Apart from traffic control, direction of motion estimation...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2016-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7565463/ |
| _version_ | 1818619628205637632 |
|---|---|
| author | Rahul Raman Pankaj Kumar Sa Banshidhar Majhi Sambit Bakshi |
| author_facet | Rahul Raman Pankaj Kumar Sa Banshidhar Majhi Sambit Bakshi |
| author_sort | Rahul Raman |
| collection | DOAJ |
| description | The paper proposes a novel approach for direction estimation of a moving pedestrian as perceived in a 2-D coordinate of field camera. The proposed direction estimation method is intended for pedestrian monitoring in traffic control systems. Apart from traffic control, direction of motion estimation is also very important in accident avoidance system for smart cars, assisted living systems, in occlusion prediction for seamless tracking in visual surveillance, and so on. The proposed video-based direction estimation method exploits the notion of perspective distortion as perceived in monocular vision of 2-D camera co-ordinate. The temporal pattern of change in dimension of pedestrian in a frame sequence is unique for each direction; hence, the dimensional change-based feature is used to estimate the direction of motion; eight discrete directions of motion are considered and the hidden Markov model is used for classification. The experiments are conducted over CASIA Dataset A, CASIA Dataset B, and over a self-acquired dataset: NITR Conscious Walk Dataset. The balanced accuracy of direction estimation for these experiments yields satisfactory results with accuracy indices as 94.58%, 90.87%, and 95.83%, respectively. The experiment also justifies with suitable test conditions about the characteristic features, such as robustness toward improper segmentation, partial occlusion, and changing orientation of head or body during walk of a pedestrian. The proposed method can be used as a standalone system or can be integrated with existing frame-based direction estimation methods for implementing a pedestrian monitoring system. |
| first_indexed | 2024-12-16T17:40:30Z |
| format | Article |
| id | doaj.art-6fa13e9ca86f4085a25b769a1a383eb8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:40:30Z |
| publishDate | 2016-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6fa13e9ca86f4085a25b769a1a383eb82022-12-21T22:22:37ZengIEEEIEEE Access2169-35362016-01-0145788580810.1109/ACCESS.2016.26088447565463Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based ApproachRahul Raman0Pankaj Kumar Sa1https://orcid.org/0000-0002-8362-3873Banshidhar Majhi2Sambit Bakshi3https://orcid.org/0000-0002-6107-114XDepartment of Computer Science & Engineering, Centre for Computer Vision and Pattern Recognition, National Institute of Technology Rourkela, Rourkela, IndiaDepartment of Computer Science & Engineering, Centre for Computer Vision and Pattern Recognition, National Institute of Technology Rourkela, Rourkela, IndiaDepartment of Computer Science & Engineering, Centre for Computer Vision and Pattern Recognition, National Institute of Technology Rourkela, Rourkela, IndiaDepartment of Computer Science & Engineering, Centre for Computer Vision and Pattern Recognition, National Institute of Technology Rourkela, Rourkela, IndiaThe paper proposes a novel approach for direction estimation of a moving pedestrian as perceived in a 2-D coordinate of field camera. The proposed direction estimation method is intended for pedestrian monitoring in traffic control systems. Apart from traffic control, direction of motion estimation is also very important in accident avoidance system for smart cars, assisted living systems, in occlusion prediction for seamless tracking in visual surveillance, and so on. The proposed video-based direction estimation method exploits the notion of perspective distortion as perceived in monocular vision of 2-D camera co-ordinate. The temporal pattern of change in dimension of pedestrian in a frame sequence is unique for each direction; hence, the dimensional change-based feature is used to estimate the direction of motion; eight discrete directions of motion are considered and the hidden Markov model is used for classification. The experiments are conducted over CASIA Dataset A, CASIA Dataset B, and over a self-acquired dataset: NITR Conscious Walk Dataset. The balanced accuracy of direction estimation for these experiments yields satisfactory results with accuracy indices as 94.58%, 90.87%, and 95.83%, respectively. The experiment also justifies with suitable test conditions about the characteristic features, such as robustness toward improper segmentation, partial occlusion, and changing orientation of head or body during walk of a pedestrian. The proposed method can be used as a standalone system or can be integrated with existing frame-based direction estimation methods for implementing a pedestrian monitoring system.https://ieeexplore.ieee.org/document/7565463/Visual surveillanceocclusion handlingpedestrian direction estimationperspective distortionhidden Markov model |
| spellingShingle | Rahul Raman Pankaj Kumar Sa Banshidhar Majhi Sambit Bakshi Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based Approach IEEE Access Visual surveillance occlusion handling pedestrian direction estimation perspective distortion hidden Markov model |
| title | Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based Approach |
| title_full | Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based Approach |
| title_fullStr | Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based Approach |
| title_full_unstemmed | Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based Approach |
| title_short | Direction Estimation for Pedestrian Monitoring System in Smart Cities: An HMM Based Approach |
| title_sort | direction estimation for pedestrian monitoring system in smart cities an hmm based approach |
| topic | Visual surveillance occlusion handling pedestrian direction estimation perspective distortion hidden Markov model |
| url | https://ieeexplore.ieee.org/document/7565463/ |
| work_keys_str_mv | AT rahulraman directionestimationforpedestrianmonitoringsysteminsmartcitiesanhmmbasedapproach AT pankajkumarsa directionestimationforpedestrianmonitoringsysteminsmartcitiesanhmmbasedapproach AT banshidharmajhi directionestimationforpedestrianmonitoringsysteminsmartcitiesanhmmbasedapproach AT sambitbakshi directionestimationforpedestrianmonitoringsysteminsmartcitiesanhmmbasedapproach |