TY - GEN
T1 - Motion trend patterns for action modelling and recognition
AU - Nguyen, Thanh Phuong
AU - Manzanera, Antoine
AU - Garrigues, Matthieu
PY - 2013/9/26
Y1 - 2013/9/26
N2 - A new method for action modelling is proposed, which combines the trajectory beam obtained by semi-dense point tracking and a local binary trend description inspired from the Local Binary Patterns (LBP). The semi dense trajectory approach represents a good trade-off between reliability and density of the motion field, whereas the LBP component allows to capture relevant elementary motion elements along each trajectory, which are encoded into mixed descriptors called Motion Trend Patterns (MTP). The combination of those two fast operators allows a real-time, on line computation of the action descriptors, composed of space-time blockwise histograms of MTP values, which are classified using a fast SVM classifier. An encoding scheme is proposed and compared with the state-of-the-art through an evaluation performed on two academic action video datasets.
AB - A new method for action modelling is proposed, which combines the trajectory beam obtained by semi-dense point tracking and a local binary trend description inspired from the Local Binary Patterns (LBP). The semi dense trajectory approach represents a good trade-off between reliability and density of the motion field, whereas the LBP component allows to capture relevant elementary motion elements along each trajectory, which are encoded into mixed descriptors called Motion Trend Patterns (MTP). The combination of those two fast operators allows a real-time, on line computation of the action descriptors, composed of space-time blockwise histograms of MTP values, which are classified using a fast SVM classifier. An encoding scheme is proposed and compared with the state-of-the-art through an evaluation performed on two academic action video datasets.
KW - Action Recognition
KW - Bag of Features
KW - Local Binary Pattern
KW - Semi dense Trajectory field
U2 - 10.1007/978-3-642-40261-6_43
DO - 10.1007/978-3-642-40261-6_43
M3 - Conference contribution
AN - SCOPUS:84884472666
SN - 9783642402609
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 360
EP - 367
BT - Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings
T2 - 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013
Y2 - 27 August 2013 through 29 August 2013
ER -