This is an action recognition composite model for the driver monitoring use case, consisting of encoder and decoder parts. The encoder model uses Video Transformer approach with MobileNetv2 encoder. It is able to recognize actions such as drinking, doing hair or making up, operating the radio, reaching behind, safe driving, talking on the phone, texting. The full list of recognized actions is located here.
| Metric | Value |
|---|---|
| Source framework | PyTorch* |
The driver-action-recognition-adas-0002-encoder model accepts video frame and produces embedding. Video frames should be sampled to cover ~1 second fragment (i.e. skip every second frame in 30 fps video).
| Metric | Value |
|---|---|
| GFlops | 0.676 |
| MParams | 2.863 |
-
name: "0" , shape: [1x3x224x224] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width
Expected color order is BGR.
The model outputs a tensor with the shape [1x512x1x1], representing embedding of processed frame.
The driver-action-recognition-adas-0002-decoder model accepts stack of frame embeddings, computed by driver-action-recognition-adas-0002-encoder, and produces prediction on input video.
| Metric | Value |
|---|---|
| GFlops | 0.147 |
| MParams | 4.205 |
- name: "0" , shape: [1x16x512] - An embedding image in the format [BxTxC],
where:
- B - batch size.
- T - Duration of input clip.
- C - dimension of embedding.
The model outputs a tensor with the shape [bx9], each row is a logits vector of performed actions.
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