Temporal Convolutional Networks for Action Segmentation and Detection

The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond. Typical approaches decouple this problem by first extracting local spatiotemporal features from video frames and then feeding them into a temporal classifier that captures high-level temporal patterns. We introduce a new class of temporal models, which we call Temporal Convolutional Networks (TCNs), that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection. Our Encoder-Decoder TCN uses pooling and upsampling to efficiently capture long-range temporal patterns whereas our Dilated TCN uses dilated convolutions. We show that TCNs are capable of capturing action compositions, segment durations, and long-range dependencies, and are over a magnitude faster to train than competing LSTM-based Recurrent Neural Networks. We apply these models to three challenging fine-grained datasets and show large improvements over the state of the art.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Action Segmentation GTEA ED-TCN F1@10% 72.2 # 24
F1@50% 56.0 # 24
Acc 64.0 # 24
Edit - # 24
F1@25% 69.3 # 24
Skeleton Based Action Recognition Varying-view RGB-D Action-Skeleton TCN Accuracy (CS) 56% # 6
Accuracy (CV I) 16% # 4
Accuracy (CV II) 43% # 5
Accuracy (AV I) 43% # 4
Accuracy (AV II) 64% # 5


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