Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera
Abstract
:1. Introduction
1.1. Related Works
1.2. Definition of Problems
2. Proposed Methods
3. Vehicle Detection
3.1. ROI Selection
3.2. Extraction of Aggregated Channel Features
3.3. Object Detection
4. Vehicle Tracking
5. Vehicle Distance Estimation
5.1. Camera Parameters Extraction
5.2. Inverse Perspective Transformation
6. Experimental Results
6.1. Results of Vehicle Detection
6.2. Results of Vehicle Distance Estimation
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Step 1. Define learning data: m objects of interest (+1) and n non-objects of interest (−1): |
Step 2. Initialization of the weight value of i-th weak classifier (h): |
Step 3. Training step (repeat for t = 1, …, C, t++) |
(1) Normalization of the weight value of the i-th learning sample of the t-th weak classifier: |
(2) Calculation of error rate () of t-th weak classifier: |
(3) Selection of a weak classifier (h) with a minimum error rate. |
(4) Updating weight values: |
Step 4. Creation of a strong classifier (H(x)) as a linear combination of weak classifiers (h): |
Criteria | Method |
---|---|
position (x, y, width, height) | position = [25, 101, 846, 267] |
aspect ratio (width/height) | 0.6939 <= ratio <= 13.1700 |
brightness Ratio (avrIntensity) | 0.3563 <= avrIntensity <= 3.7889 |
vehicle template | |
Measures | Average Precision | Log Average Error Rate | Training Time (s) | |
---|---|---|---|---|
Parameters | ||||
T = 2, S = 2 | 0.7847 | 0.4917 | 283.57 | |
T = 2, S = 4 | 0.5791 | 0.6387 | 432.47 | |
T = 4, S = 2 | 0.8755 | 0.3017 | 592.30 | |
T = 4, S = 4 | 0.8422 | 0.2947 | 680.04 | |
T = 5, S = 2 | 0.8428 | 0.2901 | 751.25 | |
T = 5, S = 4 | 0.8433 | 0.2908 | 821.34 | |
T = 6, S = 2 | 0.8292 | 0.3161 | 1257.56 | |
T = 6, S = 4 | 0.8375 | 0.2906 | 1503.33 |
Measures | Average Precision | Recall (R) | Average Processing Time, (s) | |
---|---|---|---|---|
Features | ||||
Haar | 0.7310 | 0.4941 | 0.135 | |
LBP | 0.7641 | 0.5634 | 0.126 | |
HOG | 0.8375 | 0.4775 | 0.149 | |
Our methods | 0.8755 | 0.3017 | 0.121 |
Measure | Average Distance Estimation (m) | Accuracy (%) | |
---|---|---|---|
Distance | |||
10 m | 9.8 | 98.0 | |
20 m | 21.7 | 92.2 | |
30 m | 32.7 | 91.7 | |
40 m | 43.8 | 91.3 | |
50 m | 54.8 | 91.2 |
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Kim, J.B. Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera. Symmetry 2019, 11, 1205. https://doi.org/10.3390/sym11101205
Kim JB. Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera. Symmetry. 2019; 11(10):1205. https://doi.org/10.3390/sym11101205
Chicago/Turabian StyleKim, Jong Bae. 2019. "Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera" Symmetry 11, no. 10: 1205. https://doi.org/10.3390/sym11101205