Mendeley
TY  _  JOUR
ID  -  13980228179499
TI  -  Traffic Patterns Detection in Video Surveillance Using Optical Flow and Topic Model
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Moradi Amin
AU  -  Shahbahrami Asadollah
AU  -  Akoshideh Alireza
AD  -  دانشگاه گیلان
AD  -  Gilan University
AD  -  دانشگاه فنی و حرفه ‏ای استان گیلان
Y1  -  2021
PY  -  2021
VL  -  3
IS  -  
SP  -  231
EP  -  240
KW  -  Traffic patternstopic modeloptical flowgroup sparse topical coding
DO  -  
N2  -  Research in the field of video surveillance systems has been improving because of the increasing need for intelligent monitoring, control and management. Given the large amount of data on these intelligent transportation systems, extracting patterns and automatically labeling them is a challenging task. In this paper, a topic model was used to detect and extract traffic patterns at intersections so that visual patterns are transformed into visual words. The input video is first split into clips. Then, the flow characteristics of the clips, which are based on abundant local motion vector information, are computed using optical flow algorithms and converted to visual words. After that, with a non-probabilistic topic model, the traffic patterns are extracted to the designed system by a group sparse topical coding method. These patterns represent visible motion that can be used to describe a scene by answering a behavioral question such as: Where does a vehicle go? The results of the implementation of the proposed method on the QMUL video database show that the proposed method can correctly detect and display meaningful traffic patterns such as turn left, turn right and crossing a roundabout.
UR  - rimag.ir/en/Article/28473
L1  - rimag.ir/en/Article/Download/28473
ER  -  
