INCREMENTAL LEARNING FRAMEWORK FOR OBJECT DETECTION IN VIDEOS

Number: 20170109582
Published: 2017-04-20
Filed: 2015-10-19
Assignee: Disney Enterprises, Inc.
Inventors: Kuznetsova; Alina et al.
CPC: G06V10/255; G06V10/7715; G06V20/41; G06V20/40; G06F18/2132
Verdict: Low Notable software
Source: Google Patents · FreePatentsOnline

The keeper's note

Incremental machine-learning framework for detecting objects in video, improving accuracy as new data arrives.

Abstract

Techniques disclose an incrementally expanding object detection model. An object detection tool identifies, based on an object detection model, one or more objects in a sequence of video frames. The object detection model provides an object space including a plurality of object classes. Each object class includes one or more prototypes. Each object isclassified as being an instance of one of the object classes. Each identified object is tracked across at least one of the frames. The object detection tool generates a measure ofconfidence for that object based on the tracking. Upon determining that the measure of confidence exceeds a threshold, the object detection tool adds a prototype of the instance to the object detection model.

Background

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which areillustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only exemplary embodiments and are therefore not to be considered limiting of its scope, may admit to other equally effective embodiments.

FIG. 1 illustrates an example computing environment, according to one embodiment.

FIG. 2 further illustrates the computing system described relative to FIG. 1, according to one embodiment.

FIG. 3 further illustrates the object detection tool described relative to FIGS. 1 and 2,according to one embodiment.

FIG. 4 illustrates an example of an initial object detection model, according to one embodiment.

FIG. 5 illustrates an example of an expanded object detection model, according to one embodiment.

FIG. 6 illustrates a methodof incrementally expanding an object detection model, according to one embodiment.

To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.DETAILED DESCRIPTIO

Claims

1. A method, comprising: identifying, based on anobject detection model, one or more objects in a first sequence of video frames of a plurality of sequences of video frames, wherein the object detection model provides an object space including a plurality of object classes, wherein each object class is represented byone or more prototypes, and wherein each object is classified as being an instance of oneof the object classes; and for each identified object: tracking the object across at least one of the frames, generating a measure of confidence for the object based on the tracking, wherein the measure of confidence indicates a degree that the object does not correspond to any of the one or more prototypes currently associated with the object class, and upon determining that the measure of confidence exceeds a threshold, adding a prototype representative of the instance to the object detection model. 8. A non-transitory computer-readable storage medium having instructions, which, when executed on a processor, perform an operation comprising: identifying, based on an object detection model, one or more objectsin a first sequence of video frames of a plurality of sequences of video frames, wherein the object detection model provides an object space including a plurality of object classes, wherein each object class is represented by one or more prototypes, and wherein each object is classified as being an instance of one of the object classes; and for each identified object: tracking the object across at least one of the frames, generating a measure ofconfidence for the object based on the tracking, wherein the measure of confidence indicates a degree that the object does not correspond to any of the one or more prototypes currently associated with the object class, and upon determining that the measure of confidence exceeds a threshold, adding a prototype representative of the instance to the object detection model. 15. A system, comprising: a processor; and a memory storing program code, which, when executed on the processor, performs an operation comprising: identifying, based on an object detection model, one or more objects in a first sequence of video frames of aplurality of sequences of video frames, wherein the object detection model provides an object space including a plurality of object classes, wherein each object class is represented by one or more prototypes, and wherein each object is classified as being an instance of one of the object classes, and for each identified object: tracking the object across atleast one of the frames, generating a measure of confidence for the object based on the tracking, wherein the measure of confidence indicates a degree that the object does not correspond to any of the one or more prototypes currently associated with the object class, and upon determining that the measure of confidence exceeds a threshold, adding a prototyperepresentative of the instance to the object detection model.