| Input: The original training dataset ; |
| Predefined parameters , , , and ; |
| Output: Decision tree ; |
| (1) Build a bootstrap dataset by random sampling from with replacement; |
| (2) Create a root node and set its depth to 1, then assign all cuboids in to it; |
| (3) Initialize an unsettled node queue and push the root node into ; |
| (4) while do |
| (5) Pop the first node in ; |
| (6) if depth of is larger than or cuboids assigned to belong to the same action and position then |
| (7) Label node as a leaf, and then calculate and from cuboids at node ; |
| (8) Add a triple into decision tree ; |
| (9) else |
| (10) Initialize the feature candidate set ; |
| (11) if random number then |
| (12) Add a set of randomly selected optical flow features to ; |
| (13) else |
| (14) Add a set of randomly selected HOG3D features to ; |
| (15) end if |
| (16) if random number then |
| (17) Add two-dimensional temporal context features to ; |
| (18) end if |
| (19) , generate a random number ; |
| (20) for each do |
| (21) if then |
| (22) Search for the corresponding threshold and compute information gain in terms of action labels |
| of cuboids arriving at ; |
| (23) else |
| (24) Search for the corresponding threshold and compute information gain in terms of positions of |
| cuboids arriving at ; |
| (25) end if |
| (26) if then |
| (27) ; |
| (28) end if |
| (29) end for |
| (30) Create left children node and right children node , set their depth to , and assign each cuboid |
| arriving at to or according to and ; then push node and into ; |
| (31) Add a quintuple into decision tree ; |
| (32) end if |
| (33)end while |
| (34)return Decision tree ; |
