Abstract
Optical flow methods, which estimate a dense motion field starting from a sparse one, are playing an important role in many visual learning and recognition applications. The proposed system is based only on sparse optical flow and line detector. It is able to densify the starting optical flow, reaching good performances in objective and subjective manner, using common applications like clustering and standard KITTI evaluation kit. In particular, an appreciable improvement has been achieved in terms of quantity of motion vectors grows (up to 540%). Since often in smart cameras both optical flow and lines are available, the proposed approach avoids overloading the Engine Control Unit to transmit the entire image flow and allows reducing the power consumption, realizing a real-time robust system.
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Buemi, A., Spampinato, G., Bruna, A., D’Alto, V. (2022). Densification of Sparse Optical Flow Using Edges Information. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13233. Springer, Cham. https://doi.org/10.1007/978-3-031-06433-3_22
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