• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Spatial Multilevel Optical Flow Architecture-based Dynamic Motion Estimation in Vehicular Traffic Scenarios

Vol. 12, No.12, December 31, 2018
10.3837/tiis.2018.12.020, Download Paper (Free):

Abstract

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.


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Cite this article

[IEEE Style]
Alvaro Fuentes, Sook Yoon and Dong Sun Park, "Spatial Multilevel Optical Flow Architecture-based Dynamic Motion Estimation in Vehicular Traffic Scenarios," KSII Transactions on Internet and Information Systems, vol. 12, no. 12, pp. 5978-5999, 2018. DOI: 10.3837/tiis.2018.12.020

[ACM Style]
Fuentes, A., Yoon, S., and Park, D. S. 2018. Spatial Multilevel Optical Flow Architecture-based Dynamic Motion Estimation in Vehicular Traffic Scenarios. KSII Transactions on Internet and Information Systems, 12, 12, (2018), 5978-5999. DOI: 10.3837/tiis.2018.12.020