We propose a novel deep learning approach for effective dense crowd counting by
characterizing scattered occlusions, named CSONet. CSONet recognizes the implications of
event-induced, scene-embedded, and multitudinous obstacles such as umbrellas and picket
signs to achieve an accurate crowd analysis result. CSONet is the first deep learning model
for characterizing scattered occlusions of effective dense-mode crowd counting to the best of
our knowledge. We have collected and annotated two new scattered occlusion object …