@article{
 title = {ROBUST AND FAST MOVING OBJECT DETECTION IN A NON-STATIONARY CAMERA VIA FOREGROUND PROBABILITY BASED SAMPLING},
 type = {article},
 keywords = {Index Terms— Foreground probability based sampling,background subtraction,foreground,moving object detection,non-stationary camera},
 id = {3ded3c9a-37db-37df-b4c7-4e0ed75e7b34},
 created = {2017-08-16T18:53:25.931Z},
 file_attached = {true},
 profile_id = {47354232-dc6c-341e-8f5d-b4fb19349495},
 group_id = {2b397b4f-2cb3-330b-a507-8e6163be955c},
 last_modified = {2017-08-16T18:53:27.060Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {fc79bdea-30ac-481c-8bf0-fae6a1a5be1a},
 private_publication = {false},
 abstract = {This paper proposes a robust and fast scheme to detect mov-ing objects in a non-stationary camera. The state-of-the art methods still do not give a satisfactory performance due to drastic frame changes in a non-stationary camera. To im-prove the robustness in performance, we additionally use the spatio-temporal properties of moving objects. We build the foreground probability map which reflects the spatio-temporal properties, then we selectively apply the detection procedure and update the background model only to the selected pixels using the foreground probability. The fore-ground probability is also used to refine the initial detection results to obtain a clear foreground region. We compare our scheme quantitatively and qualitatively to the state-of-the-art methods in the detection quality and speed. The experimental results show that our scheme outperforms all other compared methods.},
 bibtype = {article},
 author = {Yun, Kimin and Choi, Jin Young}
}

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