Biologically inspired features for scene classification in video surveillance.
Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective , and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.
Huang
Kaiqi
K
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
Tao
Dacheng
D
Yuan
Yuan
Y
Li
Xuelong
X
Tan
Tieniu
T
D001185:Artificial Intelligence; D001499:Bayes Theorem; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D014741:Video Recording / Q000379:methods*; D014796:Visual Perception