القائمة
Extract per-frame CNN features then classify the temporal sequence with stacked GRU layers and a boolean mask for variable-length video inputs
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استنادا إلى تصنيف SOC المهني
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| name | cv-cnn-rnn-video-classification |
| description | Extract per-frame CNN features then classify the temporal sequence with stacked GRU layers and a boolean mask for variable-length video inputs |
For video classification that benefits from temporal context (action recognition, deepfake detection), extract fixed-length feature vectors from each frame using a pretrained CNN (InceptionV3, ResNet), then feed the sequence to a GRU/LSTM. A boolean mask handles variable-length videos by ignoring padded positions. This two-stage approach decouples spatial feature learning from temporal modeling.
import tensorflow as tf
from tensorflow import keras
feature_extractor = keras.applications.InceptionV3(
weights="imagenet", include_top=False, pooling="avg")
MAX_SEQ = 30
FEAT_DIM = 2048
frame_input = keras.Input((MAX_SEQ, FEAT_DIM))
mask_input = keras.Input((MAX_SEQ,), dtype="bool")
x = keras.layers.GRU(16, return_sequences=True)(frame_input, mask=mask_input)
x = keras.layers.GRU(8)(x)
x = keras.layers.Dropout(0.4)(x)
x = keras.layers.Dense(8, activation="relu")(x)
output = keras.layers.Dense(1, activation="sigmoid")(x)
model = keras.Model([frame_input, mask_input], output)
model.compile(loss="binary_crossentropy", optimizer="adam")
feature_extractor.predict(frame) → (2048,) vectorTrue for real frames, False for padding