2019 Fiscal Year Research-status Report
Toward a Multi-Gait Analysis/Recognition System
| Project/Area Number |
19K24364
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| Research Institution | Osaka University |
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Principal Investigator |
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| Project Period (FY) |
2019-08-30 – 2021-03-31
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| Keywords | Multi-Gait Modeling / Dense Trajectories / Feature Representation |
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| Outline of Annual Research Achievements |
The main goal of this project is to recognize the identities of the persons while they are walking in groups. Due to the lack of sufficient multi-gait datasets that fit in my task, I currently trying to benefiting from the existing data in my lab trying to adapt it to fit in my project objectives.The current data I compiled contains both visual data and sensor data. The visual data consists of the single person walking sequences and the multi persons walking sequences. As well, the sensor data contains the time stamped readings of the attached sensor on the person torso during his/her walking. Over all more than 150 subject data are collected.
Achievements for visual data:A-Create Single person reference model: for this goal I did;
a.Extract the foreground pixels for from all single person walking sequences.
b.Extract the dense trajectories and optical flow information from single gait sequences. B-For multi-person walking sequences (a).Match the multi person sequences with the corresponding single person’s sequences.(b).I applied the recent Multi-objects tracking and segmentation technique to segment and track each person within the walking group separately.
Achievements for sensor data (1)-I am working synchronizing the timestamps of both the image sequences and sensor data. As the sensor data come into one time-stamped sequence which mix the single walking with the group walking sequence. Therefore. (2) I have visualized the data trying to separate the single sensor data to create the reference model.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Due to the lack of multi-gait dataset, I spent some time to create my own dataset the is already compiled from the existing data in my lab.
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| Strategy for Future Research Activity |
1-Use both the extracted visual and sensor data of the single walking sequences to build a reference model for each subject.
2-Extract the bounding boxes for each person within walking group.
3-Extract the dense trajectories for the walking group sequences and associate them to each corresponding bounding box.
4-Propose a feature representation method to create global feature descriptor for each walking person in both single and group walking sequences.
5-Once the global feature descriptor for a subject within the group is created, I can measure the pairwise similarity between the single gait reference model and the corresponding extracted descriptor from multi-gait sequences.
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| Causes of Carryover |
for support publication and travel expenses relevant to the project and the coming months
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