Video object classification with object size calibration
A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.
1. A method of improving performance of video analytics for a camera system in response to a detection preference of a system user, comprising:
receiving image data representing multiple images of a scene of a field of view of the camera system, the multiple images including representations of multiple objects, a first set of the multiple objects having members of an object class, and a second set of the multiple objects not having members of the object class;
using video analytics implemented with a general classifier that performs general classifier steps in analyzing the received image data to produce a general classification determination classifying the multiple objects as either members or non-members of the object class;
generating mistake metadata in response to acknowledgement by the system user that the general classification determination resulted in a mistaken classification determination based on the detection preference of the system user; and
improving video analytics performance based on the mistake metadata by performing a specialization step in addition to the general classification steps performed, the specialization step producing for presentation to the system user a specialized classification determination of the multiple objects classified by the general classifier so as to reduce a number of future mistaken classification determinations.
2. The method according to claim 1 , wherein classifying the multiple objects generates a confidence parameter for each object among the multiple objects, the confidence level representing a measure of confidence that the each object among the multiple objects is a member of the object class.
3. The method according to claim 2 wherein the confidence level is used to determine whether to include a classified object in the multiple objects for presentation to the system user.
4. The method according to claim 1 , wherein the general classifier is operable to classify an object as a human or non-human.
5. The method according to claim 1 , wherein the general classifier is operable to classify an object as a vehicle or non-vehicle.
6. A camera system comprising:
video analytics for processing image data representing multiple images of a scene of a field of view of the camera system, the multiple images including representations of multiple objects, a first set of the multiple objects having members of an object class, and a second set of the multiple objects not having members of the object class, the video analytics comprising:
a general classifier for performing general classifier steps in analyzing the received image data to produce a general classification determination classifying the multiple objects as either members or non-members of the object class;
wherein the video analytics is operable to generate mistake metadata in response to acknowledgement by the system user that the general classification determination resulted in a mistaken classification determination based on the detection preference of the system user; and
wherein the video analytics is further operable to improve performance based on the mistake metadata by performing a specialization step in addition to the general classification steps performed, the specialization step producing for presentation to the system user a specialized classification determination of the multiple objects classified by the general classifier so as to reduce a number of future mistaken classification determinations.
7. The method according to claim 6 , wherein classifying the multiple objects generates a confidence parameter for each object among the multiple objects, the confidence level representing a measure of confidence that the each object among the multiple objects is a member of the object class.
8. The method according to claim 7 wherein the confidence level is used to determine whether to include a classified object in the multiple objects for presentation to the system user.
9. The method according to claim 6 , wherein the general classifier is operable to classify an object as a human or non-human.
10. The method according to claim 6 , wherein the general classifier is operable to classify an object as a vehicle or non-vehicle.