Performance Evaluation of Camera-Based Time to Collision Calculation with Different Detectors&Descriptors

Abstract

Nowadays, the demand for producing and using autonomous vehicle is increasing. Due to the latest developments in technology, the capabilities of these vehicles in accident prevention are increasing. As a result of the accuracy of these capabilities, it is very important because it is human life. In today’s technology, the collision time calculation called TTC (Time to Collision) can be done in two different ways. The first of these methods is lidar-based calculation. In this paper TTC will be calculated using the camera-based method with different combinations of detectors and descriptors. Pros and cons of these methods will be discussed. The aim of this paper is to expose an exacting performance for related methods, especially its diverse combinations are used matching. In these experiments images are used for 10 images taken from real time traffic scenario of preceding vehicle. This paper includes seven methods for detectors and 6 methods for descriptors. These detectors and descriptors are used in 42 different combinations. The analysis includes four parameters such as total keypoint detection, total matches, total time in ms and performance ratio which is total matches divided by total time.

Keywords

• Autonomous Vehicles
• Image Processing
• Lidar
• Detector

Farklı Dedektörler ve Tanımlayıcılar ile Kamera Tabanlı Çarpışma Süresinin Hesaplanmasının Performans Değerlendirmesi

Abstract

Günümüz otonom araç üretme ve kullanma talebi giderek artmaktadır. Teknolojideki gelişmeler nedeniyle bu araçların kaza önleme konusundaki yetenekleri de aynı oranda artmaktadır. Bu yeteneklerin doğruluğunun sonucu olarak insan hayatı söz konusu olduğunda oldukça önemlidir. Günümüz teknolojisinde TTC adı verilen çarpışma süresi hesabı iki farklı şekilde yapılabilmektedir. Bu yöntemden ilki lidar tabanlı hesaplamadır. Bu yazıda TTC, farklı dedektör ve tanımlayıcı kombinasyonları ile kamera tabanlı yöntem kullanılarak hesaplanacaktır. Bu bildirinin amacı, özellikle çeşitli kombinasyonların eşleştirilmesi için kullanılan yöntemler için hızlı. Bu deneylerde, öndeki aracın gerçek zamanlı trafik senaryosundan alınan 10 görüntü kullanılmıştır. Bu bildiri, dedektörler için yedi yöntem ve tanımlıyıcılar için 6 yöntem içermektedir. Bu dedektörler ve tanımlayıcılar 42 farklı kombinasyonda kullanılmaktadır. Analiz toplam anahtar nokta tespiti, toplam eşleşmeler, mili-saniye cinsinden toplam süre ve toplam eşleşmelerin toplam süreye bölünmesiyle elde edilen performans oranı gibi dört parametreyi içerir.

Keywords

• Otonom Araçlar
• İmge İşleme
• Dedektör
• Tanımlayıcı

References

1. Min, K. W., Han, S. J., Lee, D. J., Choi, D. S., Sung, K. B., & Choi, J. D. (2019). SAE level 3 autonomous driving technology of the ETRI. 2019 International Conference on Information and Communication Technology Convergence (ICTC).
2. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
3. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3), 346–359.
4. Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011). Brisk: Binary robust invariant scalable keypoints. 2011 International Conference on Computer Vision.
5. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). Orb: An efficient alternative to SIFT or surf. 2011 International Conference on Computer Vision.
6. Remondino, F. (2021). Detectors and descriptors for photogrammetric applications. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
7. Dahl, A. L., Aanæs, H., & Pedersen, K. S. (2011). Finding the best feature detector-descriptor combination. 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.
8. Li, Q., Mao, Y., Wang, Z., & Xiang, W. (2009). Robust real-time detection of abandoned and removed objects. 2009 Fifth International Conference on Image and Graphics.

9. Seki, Y., Ohya, J., & Miyoshi, M. (1999). Collision avoidance system for vehicles applying model predictive control theory. Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).
10. Mukhtar, A., Xia, L., & Tang, T. B. (2015). Vehicle detection techniques for collision avoidance systems: A Review. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2318–2338.
11. Matsuzaki, T., Kameda, H., Tsujimichi, S., & Kosuo, Y. (1999). Manoeuvring target tracking using constant velocity and constant angular velocity model. SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456).
12. Chen, W., Zhou, G., & Giannakis, G. B. (1995). Velocity and acceleration estimation of Doppler Weather Radar/LIDAR signals in Colored Noise. 1995 International Conference on Acoustics, Speech, and Signal Processing.
13. Bosnak, M., & Skrjanc, I. (2017). Efficient time-to-collision estimation for a braking supervision system with Lidar. 2017 3rd IEEE International Conference on Cybernetics (CYBCONF).
14. Shen, Q. (2021). Design of backward collision warning and avoidance system when on-street parking using LIDAR. 2021 2nd International Conference on Computing and Data Science (CDS).
15. Rosten, E., & Drummond, T. (2005). Fusing points and lines for high performance tracking. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
16. [Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. Computer Vision – ECCV 2006, 430–443. Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010). Brief: Binary robust independent elementary features. Computer Vision – ECCV 2010, 778–792.