- A smartphone-based road anomaly detection system is presented, in which obstacle avoidance behaviors are categorized into three classes. The three classes include: (1) returning to the same line in the vicinity of avoiding an obstacle; (2) going straight after avoiding an obstacle; and (3) reversing his/her course; which may indicate the impact of the obstacle on pedestrians. The three classes may indicate the severity of obstacles, which would be helpful for an administrative entity to plan a repair schedule.
- Twenty nine classification features are defined based on the characteristics of the azimuth change of each class. The relevance of the features is evaluated.
- We extensively analyze the effects of various factors on the recognition performance. This includes the individuals who provide data for training classifiers and the position of sensors (i.e., smartphones) on their bodies, as well as the size of target obstacles.
2. Related Work
3. Avoidance Behavior Recognition
3.1. System Overview
3.2. Avoidance Behavior Modeling
3.3. Avoidance Behavior Recognition
3.3.1. Waveform Shaping
|Algorithm 1 Calculate Azimuth Change Relative to the First Value in a Segment.|
3.3.2. Behavior Classification
4. Offline Experiment
4.2. Basic Classification Performance
4.2.2. Result and Analysis
4.3. Feature Relevance
4.3.2. Result and Analysis
4.4. Person Dependency
4.4.2. Result and Analysis
4.5. Effect of Sensor Storing Position
4.5.2. Result and Analysis
4.6. Robustness to Unknown Obstacle Size
4.6.2. Result and Analysis
- A 10-fold CV showed an average classification performance with an F-measure of 0.89 for six avoidance behaviors.
- The recognition system could handle the obstacle sizes of 0.2 to 1.5 m. Untrained sizes of obstacle avoidance were also recognized with an F-measure of 0.94.
- A user-independent classifier classified six avoidance behaviors with an F-measure of 0.81. The possibility of improving a user-independent classification by choosing classifiers trained by compatible persons was shown.
- Features resulting from (1) splitting a segment into the first half and the second half and (2) considering the monotonicity of change effectively recognized avoidance behaviors.
- The performance slightly depends on the sensor (smartphone) storing position on the body. Selecting a classifier for a particular position improves the performance. To reduce the cost of data collection, only the data from “hand” and “trousers back pocket” need be collected.
Conflicts of Interest
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|sum of absolute difference of|
|both ends of 10 subsegments|
|Types of avoidance||, ,|
|Size of obstacles (d)||0.2, 0.5, 0.7, 1.0, 1.5 m|
|Storing positions||hand (texting), trousers front pocket, trousers back pocket, chest pocket|
|Subjects||7 males and 2 females in their 20s|
|Number of trials||6 times per condition|
|Terminal||Samsung, Galaxy Nexus|
|Android version||Android 4.2.1|
|Sampling rate||10 Hz|
|H||538||trousers front pocket||966|
|I||534||trousers back pocket||968|
|Bayesian network||-Q K2 “-P 1 -S BAYES” -E SimpleEstimator “-A 0.5”|
|MLP||-L 0.3 -M 0.2 -N 500 -V 0 -E 20 -H a|
|SMO||-C 1.0 -P 1.0E-12 -K “PolyKernel -E 1.0 -C 250007”|
|J48||-C 0.25 -M 2|
|Random forest||-I 100 -K 0|
|Trained with\Test with||(1)||(2)||(3)||(4)||Average|
|(1) Hand (texting)||0.86||0.82||0.82||0.91||0.86|
|(2) Trousers front pocket||0.85||0.85||0.86||0.89||0.86|
|(3) Trousers back pocket||0.83||0.85||0.88||0.88||0.86|
|(4) Chest pocket||0.86||0.83||0.80||0.91||0.85|
|(0) Tuned classifier for each position||0.87|
|(1) Single classifier with the dataset from all positions||0.89|
|(2) Sharing classifiers with some positions||0.88|
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