Semantic representation module of a machine-learning engine in a video analysis system
A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.
1. A computer-implemented method for processing data related to a scene depicted in a sequence of video frames, the method comprising:
receiving input data describing an object detected in the scene, wherein the input data includes at least a classification for the object;
identifying one or more primitive events, related to a behavior engaged in by the object depicted in the sequence of video frames and wherein each primitive event has an assigned primitive event symbol;
generating, for the object, a primitive event symbol stream which includes the primitive event symbols corresponding to the primitive events identified for a respective object;
forming a first vector representation of the object; and
comparing the first vector representations to a stored vector representation to define a similarity measure.
2. The computer-implemented method of claim 1 , further comprising, applying a singular value decomposition (SVD) to the first vector representation to generate a second vector representation from the first vector representation, wherein the second vector representation reduces the dimensionality of the first vector representation.
3. The computer-implemented method of claim 1 , wherein the classification for the object specifies that the object depicts one of a vehicle object, a person object, or an unknown object.
4. The computer-implemented method of claim 3 , wherein the object is classified as a person, and wherein the input data further includes a posture of the person as depicted in the sequence of video frames.
5. The computer-implemented method of claim 3 , wherein the object is classified as a vehicle, and wherein the input data further comprises behavior engaged in by the vehicle that includes one or more of appearing, moving, turning and stopping.
6. The computer-implemented method of claim 3 , wherein the object is classified as a person, and wherein the input data further comprises behavior engaged in by the person that includes one or more of appearing, moving, turning and stopping.
7. The computer-implemented method of claim 3 , wherein the object is classified as an unknown object, and wherein the input data further comprises behavior engaged in by the unknown object that includes one or more of appearing, moving, turning and stopping.
8. The computer-implemented method of claim 1 , wherein comparing includes comparing the first vector representation to the stored vector representation with a machine learning engine.
9. A non-transitory computer-readable medium containing a program, which, when executed on a processor is configured to perform an operation for processing data related to a scene depicted in a sequence of video frames, comprising:
receiving input data describing an object detected in the scene, wherein the input data includes at least a classification for the object;
identifying one or more primitive events, related to a behavior engaged in by the object depicted in the sequence of video frames and wherein each primitive event has an assigned primitive event symbol;
generating, for the object, a primitive event symbol stream which includes the primitive event symbols corresponding to the primitive events identified for the object;
forming a first vector representation of the object; and
comparing the first vector representations to a stored vector representation to define a similarity measure.
10. The non-transitory computer-readable medium of claim 9 , further comprising, applying a singular value decomposition (SVD) to the first vector representation to generate a second vector representation from the first vector representation, wherein the second vector representation reduces the dimensionality of the first vector representation.
11. The non-transitory computer-readable medium of claim 9 , wherein the classification for the object specifies that the object depicted in the sequence of video frames depicts one of a vehicle object, a person object, or an unknown object.
12. The non-transitory computer-readable medium of claim 11 , wherein the object is classified as a person, and wherein the input data further includes a posture of the person as depicted in the sequence of video frames.
13. The computer-implemented method of claim 9 , wherein comparing includes comparing the first vector representation with a machine learning engine.
14. A system, comprising:
a video input source;
a processor; and
a memory storing computer instructions, which, when executed on the processor configure the processor to:
input data describing a scene depicted in a sequence of video frames, the input data including at least a classification for an object in a sequence of the video frames;
identify one or more primitive events, wherein each primitive event provides a semantic value describing a behavior engaged in by the object depicted in the sequence of video frames and wherein each primitive event has an assigned primitive event symbol;
generate, for the object, a primitive event symbol stream which includes the primitive event symbols corresponding to the primitive events identified for the object;
form a first vector representation of the object; and
compare the first vector representations to a stored vector representation to define a similarity measure.
15. The system of claim 14 , wherein the processor is further configured to apply a singular value decomposition (SVD) to each first vector representation to generate a second vector representation from the first vector representation, wherein the second vector representation reduces the dimensionality of the first vector representation.
16. The system of claim 14 , wherein the classification for the object specifies that the object depicted in the sequence of video frames depicts one of a vehicle object, a person object, or an unknown object.
17. The system of claim 16 , wherein the object is classified as a person, and wherein the input data further includes a posture of the person as depicted in the sequence of video frames.
18. The system of claim 16 , wherein the object is classified as a vehicle, and wherein the behavior engaged in by the vehicle includes one or more of appearing, moving, turning and stopping.
19. The system of claim 16 , wherein the object is classified as a person, and wherein the behavior engaged in by the person includes one or more of appearing, moving, turning and stopping.
20. The system of claim 14 , wherein a machine learning engine is employed to compare the first vector representation to the stored vector representation.