基於CENTRIST特徵和隨機過程實現行人偵測

A method of pedestrian detection based on CENTRIST descriptor and stochastic process is proposed in this thesis. In related work such as C4 and Peng’s method, they use only single image as input, regardless driving is a continuous process. In our work, we will use sequential data and use stochastic process to help determine the possibility of pedestrian appearance. We use the training set cut from our own database built by driving recorder Papago P3 to train SVM models to be our basic object detector. Our experimental results show that our method outperforms C4 and Peng’s method in execution time and comparable accuracy by applying stochastic determination.

並列關鍵字

Pedestrian detection ； CENTRIST descriptor ； stochastic process

參考文獻

[1] J. Begard, N. Allezard, and P. Sayd, “Real-Time Human Detection in Urban Scenes: Local Descriptors and Classifiers Selection with AdaBoost-like Algorithms,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, pp. 1–8, 2008.

[2] Z. Lin, G. Hua, L. S. Davis, and C. Park, “Multi-Scale Shared Features for Cascade Object Detection,” in Proceedings of IEEE International Conference on Image Processing, Orlando, FL, pp. 1865–1868, 2012.

[3] D. Mitzel, P. Sudowe, and B. Leibe, “Real-Time Multi-Person Tracking with Time-Constrained Detection,” in Proceedings of the British Machine Vision Conference, Dundee, Scotland, UK, pp. 104.1–104.11, 2011.

[6] M. Souded and F. Bremond, “Optimized Cascade of Classifiers for People Detection Using Covariance Features,” in Proceedings of International Conference on Computer Vision Theory and Applications, Barcelona, Espagne, pp. 1–7, 2013.

[7] P. Viola and M. Jones, “Robust Real-Time Object Detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2002.

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基於CENTRIST特徵和隨機過程實現行人偵測

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