TY - GEN
T1 - Dense flow field algorithm using binary descriptor and modified energy function
AU - Pae, Dong Sung
AU - Oh, Hyeon Chan
AU - Park, Sang Kyoo
AU - Kang, Tae Koo
AU - Lim, Myo Taeg
N1 - Funding Information:
This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education under Grant NRF-2016R1D1A1B01016071; by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education under Grant NRF-2016R1D1A1B03936281.
Funding Information:
*This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education under Grant NRF-2016R1D1A1B01016071; by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education under Grant NRF-2016R1D1A1B03936281.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - In this paper, we describe a Dense Flow-Field algorithm for moving detection of an object using a binary descriptor and a modified energy function. Among the moving detection algorithms, a Dense SIFT-Flow algorithm is recently introduced. In the conventional Dense SIFT-Flow, a SIFT descriptor and an energy function are employed to make the flow vectors containing the movement information of each pixel at entire image. The matching process in the conventional SIFT-Flow algorithm uses descriptor information and a message-passing method in a coarse-to-fine scheme. Although the matching performance of the Dense SIFT-Flow is good for detecting the movement of each pixel, large computational time is needed. To reduce the complexity of the description part, the proposed method employs a binary descriptor. The process of the binary descriptor is simple enough to reduce the complexity. In addition, the energy function in the conventional Dense Flow-Field must be modified for the binary descriptor as replacing the unfair displacement term of the conventional energy function with a fair displacement term. From the experimental results, we can know that the proposed method is faster than the conventional method with respect to making flow field and more robust with respect to diagonal movements.
AB - In this paper, we describe a Dense Flow-Field algorithm for moving detection of an object using a binary descriptor and a modified energy function. Among the moving detection algorithms, a Dense SIFT-Flow algorithm is recently introduced. In the conventional Dense SIFT-Flow, a SIFT descriptor and an energy function are employed to make the flow vectors containing the movement information of each pixel at entire image. The matching process in the conventional SIFT-Flow algorithm uses descriptor information and a message-passing method in a coarse-to-fine scheme. Although the matching performance of the Dense SIFT-Flow is good for detecting the movement of each pixel, large computational time is needed. To reduce the complexity of the description part, the proposed method employs a binary descriptor. The process of the binary descriptor is simple enough to reduce the complexity. In addition, the energy function in the conventional Dense Flow-Field must be modified for the binary descriptor as replacing the unfair displacement term of the conventional energy function with a fair displacement term. From the experimental results, we can know that the proposed method is faster than the conventional method with respect to making flow field and more robust with respect to diagonal movements.
UR - http://www.scopus.com/inward/record.url?scp=85050850102&partnerID=8YFLogxK
U2 - 10.1109/SII.2017.8279356
DO - 10.1109/SII.2017.8279356
M3 - Conference contribution
AN - SCOPUS:85050850102
T3 - SII 2017 - 2017 IEEE/SICE International Symposium on System Integration
SP - 1016
EP - 1021
BT - SII 2017 - 2017 IEEE/SICE International Symposium on System Integration
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE/SICE International Symposium on System Integration, SII 2017
Y2 - 11 December 2017 through 14 December 2017
ER -