/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { Pose } from '../types';
import { TrackerConfig } from './interfaces/config_interfaces';
import { Tracker } from './tracker';
/**
 * KeypointTracker, which tracks poses based on keypoint similarity. This
 * tracker assumes that keypoints are provided in normalized image
 * coordinates.
 */
export declare class KeypointTracker extends Tracker {
    private readonly keypointThreshold;
    private readonly keypointFalloff;
    private readonly minNumKeyoints;
    constructor(config: TrackerConfig);
    /**
     * Computes similarity based on Object Keypoint Similarity (OKS). It's
     * assumed that the keypoints within each `Pose` are in normalized image
     * coordinates. See `Tracker` for more details.
     */
    computeSimilarity(poses: Pose[]): number[][];
    /**
     * Computes the Object Keypoint Similarity (OKS) between a pose and track.
     * This is similar in spirit to the calculation used by COCO keypoint eval:
     * https://cocodataset.org/#keypoints-eval
     * In this case, OKS is calculated as:
     * (1/sum_i d(c_i, c_ti)) * \sum_i exp(-d_i^2/(2*a_ti*x_i^2))*d(c_i, c_ti)
     * where
     *   d(x, y) is an indicator function which only produces 1 if x and y
     *     exceed a given threshold (i.e. keypointThreshold), otherwise 0.
     *   c_i is the confidence of keypoint i from the new pose
     *   c_ti is the confidence of keypoint i from the track
     *   d_i is the Euclidean distance between the pose and track keypoint
     *   a_ti is the area of the track object (the box covering the keypoints)
     *   x_i is a constant that controls falloff in a Gaussian distribution,
     *    computed as 2*keypointFalloff[i].
     * @param pose A `Pose`.
     * @param track A `Track`.
     * @returns The OKS score between the pose and the track. This number is
     * between 0 and 1, and larger values indicate more keypoint similarity.
     */
    private oks;
    /**
     * Computes the area of a bounding box that tightly covers keypoints.
     * @param Keypoint[] An array of `Keypoint`s.
     * @returns The area of the object.
     */
    private area;
}