Activity recognition systems and methods
An activity recognition system is disclosed. A plurality of temporal features is generated from a digital representation of an observed activity using a feature detection algorithm. An observed activity graph comprising one or more clusters of temporal features generated from the digital representation is established, wherein each one of the one or more clusters of temporal features defines a node of the observed activity graph. At least one contextually relevant scoring technique is selected from similarity scoring techniques for known activity graphs, the at least one contextually relevant scoring technique being associated with activity ingestion metadata that satisfies device context criteria defined based on device contextual attributes of the digital representation, and a similarity activity score is calculated for the observed activity graph as a function of the at least one contextually relevant scoring technique, the similarity activity score being relative to at least one known activity graph.
1. An activity recognition method using at least one processor and at least one memory, the method comprising:
receiving a digital representation comprising at least video and audio data of an observable activity of at least one object;
generating from the digital representation, using at least one feature detection algorithm, a plurality of features related to the observable activity;
establishing an observed activity data object based on the plurality of features;
determining a similarity for the observed activity data object relative to at least one known activity data object based on a context relevant to the plurality of features;
accessing an activity recognition results set based on the similarity; and
triggering an action based on the activity recognition results set,
wherein a known activity associated with the at least one known activity data object includes at least one of a motion of a device, a tracked location of the device, an audio signal, and a movement.
2. The method of claim 1 , wherein the known activity includes at least two of the motion, the tracked location, the audio signal, and the movement.
3. The method of claim 2 , wherein the known activity includes the motion and the tracked location.
4. The method of claim 1 , wherein the known activity further includes a verbal command.
5. The method of claim 1 , wherein the known activity further includes at least one of a body movement, an interaction, a hand movement, and a facial movement or expression.
6. The method of claim 1 , wherein the device comprises an activity recognition device.
7. The method of claim 1 , wherein the context comprises a virtual machine environment.
8. The method of claim 7 , wherein the virtual machine environment comprises a virtual gaming environment.
9. The method of claim 8 , wherein the at least one object comprises actions or interactions among multiple objects.
10. The method of claim 9 , wherein the multiple objects are spaced apart from one another.
11. The method of claim 1 , wherein the at least one known activity data object further comprises at least a part of a template for interactions.
12. The method of claim 11 , wherein the at least a part of a template for interactions is configured for an online gaming interaction involving at least one game-related activity.
13. The method of claim 1 , wherein triggering an action comprises triggering a virtual action in a gaming environment.
14. The method of claim 1 , wherein the context comprises location data.
15. The method of claim 1 , wherein the digital representation further comprises at least one of image data, still image data, accelerometer data, tactile data, kinesthetic data, temperature data, kinematic data, 3D registration data, and radio signal or wireless data.
16. The method of claim 15 , wherein the image data comprises at least one of ultrasound, infrared, and visible spectrum data.
17. The method of claim 1 , wherein the at least one feature detection algorithm includes one of the following: a scale-invariant feature transform (SIFT), Fast Retina Keypoint (FREAK), Histograms of Oriented Gradient (HOG), Speeded Up Robust Features (SURF), DAISY, Binary Robust Invariant Scalable Keypoints (BRISK), FAST, Binary Robust Independent Elementary Features (BRIEF), Harris Corners, Edges, Gradient Location and Orientation Histogram (GLOH), Energy of image Gradient (EOG), and Transform Invariant Low-rank Textures (TILT) feature detection algorithm.
18. The method of claim 1 , wherein the digital representation comprises video and audio data captured over a time period or within a time frame.
19. The method of claim 1 , wherein at least some of the plurality of features describe a temporal or spatial relationship among comparable events in time.
20. The method of claim 1 , further comprising:
converting aspects of the digital representation to an observed activity graph; and
comparing the observed activity graph to known activity graphs.
21. The method of claim 1 , further comprising determining contextual relevance based on ingestion metadata.
22. The method of claim 21 , further comprising selecting the ingestion metadata used to determine contextual relevance based on one or more domain-specific attributes.
23. The method of claim 21 , wherein the ingestion metadata conforms to a defined attribute namespace.
24. The method of claim 1 , further comprising:
recognizing one or more objects in the digital representation using at least some of the plurality of features; and
retrieving object information related to the one or more recognized objects.
25. The method of claim 24 , further comprising using the object information to determine contextual relevance.
26. The method of claim 1 , wherein the similarity includes at least one of a Euclidean distance, linear kernel, polynomial kernel, Chi-squared kernel, Cauchy kernel, histogram intersection kernel, Hellinger's kernel, Jensen-Shannon kernel, hyperbolic tangent (sigmoid) kernel, rational quadratic kernel, multiquadratic kernel, inverse multiquadratic kernel, circular kernel, spherical kernel, wave kernel, power kernel, log kernel, spline kernel, Bessel kernel, generalized T-Student kernel, Bayesian kernel, wavelet kernel, radial basis function (RBF), exponential kernel, Laplacian kernel, ANOVA kernel and B-spline kernel function.
27. The method of claim 1 , further comprising selecting the similarity according to a data modality.
28. The method of claim 1 , wherein the similarity reflects a relative confidence of data from each of a plurality of sensing modalities.
29. The method of claim 1 , wherein the activity recognition results set comprises at least one of an activity identifier, a search result, a classification, a recommendation, an anomaly, a warning, a segmentation, a command, a ranking, context relevant information, content information, and an action prediction.
30. The method of claim 29 , wherein the action prediction is based on variations of known activities.
31. The method of claim 1 , wherein triggering the action comprises executing a command.
32. The method of claim 1 , wherein triggering the action comprises generating an alert.
33. The method of claim 1 , wherein the video and audio data relates to a volumetric space.
34. An activity recognition device having a processor, wherein, upon execution of software instructions stored on a non-transitory computer readable medium, the processor:
captures a digital representation comprising at least video and audio data of an observable activity of at least one object;
generates from the digital representation, using at least one feature detection algorithm, a plurality of features related to the observable activity;
establishes an observed activity data object based on the plurality of features;
determines a similarity for the observed activity data object relative to at least one known activity data object based on a context relevant to the plurality of features;
accesses an activity recognition results set based on the similarity; and
triggers an action based on the activity recognition results set,
wherein a known activity associated with the at least one known activity data object includes at least one of a motion of a device, a tracked location of the device, an audio signal, and a movement.
35. A non-transitory computer readable medium comprising instructions executable by a computer processor to perform activity recognition by performing operations comprising:
capturing a digital representation comprising at least video and audio data of an observable activity of at least one object;
generating from the digital representation, using at least one feature detection algorithm, a plurality of features related to the observable activity;
establishing an observed activity data object based on the plurality of features;
determining a similarity for the observed activity data object relative to at least one known activity data object based on a context relevant to the plurality of features;
accessing an activity recognition results set based on the similarity; and
triggering an action based on the activity recognition results set,
wherein a known activity associated with the at least one known activity data object includes at least one of a motion of a device, a tracked location of the device, an audio signal, and a movement.