model.py

from __future__ import division

from keras.models import Model
from keras.layers import *
from keras.layers.core import *
from keras.layers.convolutional import *


def one_obj(frame_l=16, joint_n=15, joint_d=2):
    input_joints = Input(name='joints', shape=(frame_l, joint_n, joint_d))
    input_joints_diff = Input(name='joints_diff', shape=(frame_l, joint_n, joint_d))

    ##########branch 1##############

    x = Conv2D(filters=32, kernel_size=(1, 1), padding='same')(input_joints)
    x = BatchNormalization()(x)
    x = LeakyReLU()(x)

    x = Conv2D(filters=16, kernel_size=(3, 1), padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU()(x)

    x = Permute((1, 3, 2))(x)

    x = Conv2D(filters=16, kernel_size=(3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU()(x)

    ##########branch 1##############

    ##########branch 2##############Temporal difference

    x_d = Conv2D(filters=32, kernel_size=(1, 1), padding='same')(input_joints_diff)
    x_d = BatchNormalization()(x_d)
    x_d = LeakyReLU()(x_d)

    x_d = Conv2D(filters=16, kernel_size=(3, 1), padding='same')(x_d)
    x_d = BatchNormalization()(x_d)
    x_d = LeakyReLU()(x_d)

    x_d = Permute((1, 3, 2))(x_d)

    x_d = Conv2D(filters=16, kernel_size=(3, 3), padding='same')(x_d)
    x_d = BatchNormalization()(x_d)
    x_d = LeakyReLU()(x_d)

    ##########branch 2##############

    x = concatenate([x, x_d], axis=-1)

    x = Conv2D(filters=32, kernel_size=(1, 1), padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU()(x)
    x = MaxPool2D(pool_size=(2, 2))(x)
    x = Dropout(0.1)(x)

    x = Conv2D(filters=64, kernel_size=(1, 1), padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU()(x)
    x = MaxPool2D(pool_size=(2, 2))(x)
    x = Dropout(0.1)(x)

    x = Flatten()(x)
    x = Dropout(0.1)(x)

    x = Dense(256)(x)
    x = BatchNormalization()(x)
    x = LeakyReLU()(x)
    x = Dropout(0.1)(x)

    x = Dense(6, activation='softmax')(x)

    model = Model([input_joints, input_joints_diff], x)

    return model