A Tracking-by-Detection System for Pedestrian Tracking Using Deep Learning Technique and Color Information


Mai Thanh Nhat Truong, Sanghoon Kim, Journal of Information Processing Systems Vol. 15, No. 4, pp. 1017-1028, Aug. 2019  

10.3745/JIPS.04.0132
Keywords: Color Distribution, Convolutional Neural Network, Pedestrian Tracking, Tracking-by-Detection
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Abstract

Pedestrian tracking is a particular object tracking problem and an important component in various visionbased applications, such as autonomous cars and surveillance systems. Following several years of development, pedestrian tracking in videos remains challenging, owing to the diversity of object appearances and surrounding environments. In this research, we proposed a tracking-by-detection system for pedestrian tracking, which incorporates a convolutional neural network (CNN) and color information. Pedestrians in video frames are localized using a CNN-based algorithm, and then detected pedestrians are assigned to their corresponding tracklets based on similarities between color distributions. The experimental results show that our system is able to overcome various difficulties to produce highly accurate tracking results.


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Cite this article
[APA Style]
Mai Thanh Nhat Truong and Sanghoon Kim (2019). A Tracking-by-Detection System for Pedestrian Tracking Using Deep Learning Technique and Color Information. Journal of Information Processing Systems, 15(4), 1017-1028. DOI: 10.3745/JIPS.04.0132.

[IEEE Style]
M. T. N. Truong and S. Kim, "A Tracking-by-Detection System for Pedestrian Tracking Using Deep Learning Technique and Color Information," Journal of Information Processing Systems, vol. 15, no. 4, pp. 1017-1028, 2019. DOI: 10.3745/JIPS.04.0132.

[ACM Style]
Mai Thanh Nhat Truong and Sanghoon Kim. 2019. A Tracking-by-Detection System for Pedestrian Tracking Using Deep Learning Technique and Color Information. Journal of Information Processing Systems, 15, 4, (2019), 1017-1028. DOI: 10.3745/JIPS.04.0132.