Large margin high-order deep learning with auxiliary tasks for video-based anomaly detection

1. A video camera for video-based anomaly detection, comprising:

at least one imaging sensor configured to capture one or more video sequences in a workplace environment having a plurality of machines therein;

a processor, configured to:

generate one or more predictions of an impending anomaly affecting at least one item selected from the group consisting of (i) at least one of the plurality of machines and (ii) at least one operator of the at least one of the plurality of machines, using a Deep High-Order Convolutional Neural Network (DHOCNN)-based model applied to the one or more video sequences, the DHOCNN-based model having a one-class Support Vector Machine (SVM) as a loss layer of the DHOCNN-based model; and

generate a signal for initiating an action to the at least one of the plurality of machines to mitigate expected harm to the at least one item,

wherein the processor is further configured to reduce a computational complexity of the DHOCNN-based model using a matrix factorization technique, and wherein the matrix factorization technique involves a plurality of rank-one matrices.

2. The video camera of claim 1 , further comprising a network adapter configured to transmit the signal to an object selected from the group consisting of (i) the at least of the plurality of machines, and (ii) a safety device associating with the at least one of the plurality of machines.

3. The video camera of claim 1 , further comprising a hardware port for receiving a wired communication medium over which the signal is transmitted to an object selected from the group consisting of (i) the at least of the plurality of machines, and (ii) a safety device associating with the at least one of the plurality of machines.

4. The video camera of claim 1 , wherein the processor is further configured to pre-train the DHOCNN-based model using a plurality of auxiliary tasks relating to potential dangerous conditions which generate supervision labels and guide parameter learning for the DHOCNN-based model.

5. The video camera of claim 4 , wherein pre-training the DHOCNN-based model uses back-propagation with the plurality of auxiliary tasks.

6. The video camera of claim 1 , wherein the processor is further configured to fine-tune model parameters of the DHOCNN-based model using back-propagation.

7. The video camera of claim 1 , wherein the SVM is employed in a final layer of the DHOCNN-based model.

8. The video camera of claim 1 , wherein the video-based anomaly detection is supervised video-based anomaly detection.

9. The video camera of claim 1 , wherein the DHOCNN-based model uses a plurality of high-order kernels to capture high-order feature interactions relating to the input image on which the video-based anomaly detection is performed.

10. The video camera of claim 1 , wherein the plurality of rank-one matrices are configured to explicitly model different orders of interaction among features extracted the input image.

11. The video camera of claim 1 , wherein the SVM is configured to separate data examples from an origin in a feature space and maximize a distance from a hyperplane of the feature space to the origin.

12. The video camera of claim 1 , wherein the SVM is configured to produce a binary function that captures regions in a feature space where a probability density is greatest.

13. A video camera system for video-based anomaly detection, comprising:

a plurality of video cameras, each including:

at least one imaging sensor configured to capture one or more video sequences in a workplace environment having a plurality of machines therein;

a processor, configured to:

generate one or more predictions of an impending anomaly affecting at least one item selected from the group consisting of (i) at least one of the plurality of machines and (ii) at least one operator of the at least one of the plurality of machines, using a Deep High-Order Convolutional Neural Network (DHOCNN)-based model applied to the one or more video sequences, the DHOCNN-based model having a one-class Support Vector Machine (SVM) as a loss layer of the DHOCNN-based model; and

generate a signal for initiating an action to the at least one of the plurality of machines to mitigate expected harm to the at least one item,

wherein the processor is further configured to reduce a computational complexity of the DHOCNN-based model using a matrix factorization technique, and wherein the matrix factorization technique involves a plurality of rank-one matrices.

14. The video camera system of claim 13 , wherein each of the plurality of video cameras further include a network adapter configured to transmit the signal to an object selected from the group consisting of (i) the at least of the plurality of machines, and (ii) a safety device associating with the at least one of the plurality of machines.

15. The video camera system of claim 13 , wherein each of the plurality of video cameras further include a hardware port for receiving a wired communication medium over which the signal is transmitted to an object selected from the group consisting of (i) the at least of the plurality of machines, and (ii) a safety device associating with the at least one of the plurality of machines.

16. The video camera system of claim 13 , wherein the processor in each of the plurality of video cameras is further configured to pre-train the DHOCNN-based model using a plurality of auxiliary tasks relating to potential dangerous conditions which generate supervision labels and guide parameter learning for the DHOCNN-based model.

17. The video camera system of claim 16 , wherein pre-training the DHOCNN-based model uses back-propagation with the plurality of auxiliary tasks.

18. The video camera system of claim 13 , wherein the processor in each of the plurality of video cameras is further configured to fine-tune model parameters of the DHOCNN-based model using back-propagation.