人物的轨迹

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人物的轨迹 - Amazon Rekognition

注意

终止支持通知：2025 年 10 月 31 日，AWS 将停止对 Amazon Rekognition 人物轨迹跟踪的支持。2025 年 10 月 31 日之后，您将无法再使用 Rekognition 人物轨迹跟踪功能。有关更多信息，请访问此博客文章。

Amazon Rekognition Video 可以创建视频中的人物轨迹并提供如下信息：

在跟踪人物的轨迹时人物在视频帧中的位置。
人脸标记，如左眼的位置（如果检测到）。

存储视频中的 Amazon Rekognition Video 人物轨迹跟踪是一个异步操作。要开始在视频中寻找人物路径，请致电StartPersonTracking。Amazon Rekognition Video 会将视频分析的完成状态发布到 Amazon Simple Notification Service 主题。如果视频分析成功，请致电GetPersonTracking以获取视频分析的结果。有关调用 Amazon Rekognition Video API 操作的更多信息，请参阅调用 Amazon Rekognition Video 操作。

以下过程说明如何通过存储在 Amazon S3 存储桶内的视频跟踪人物轨迹。此示例扩展了使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK)（使用 Amazon Simple Queue Service 队列获取视频分析请求的完成状态）中的代码。

检测存储在 Amazon S3 存储桶内的视频中的人员 (SDK)

执行使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK)。

将以下代码添加到您在步骤 1 中创建的类 VideoDetect。

Java


       //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
       //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)

        //Persons========================================================================
        private static void StartPersonDetection(String bucket, String video) throws Exception{
            
            
            NotificationChannel channel= new NotificationChannel()
                    .withSNSTopicArn(snsTopicArn)
                    .withRoleArn(roleArn);
            
         StartPersonTrackingRequest req = new StartPersonTrackingRequest()
                 .withVideo(new Video()
                         .withS3Object(new S3Object()
                             .withBucket(bucket)
                             .withName(video)))
                 .withNotificationChannel(channel);
                                
             
            
         StartPersonTrackingResult startPersonDetectionResult = rek.startPersonTracking(req);
         startJobId=startPersonDetectionResult.getJobId();
            
        } 
        
        private static void GetPersonDetectionResults() throws Exception{
            int maxResults=10;
            String paginationToken=null;
            GetPersonTrackingResult personTrackingResult=null;
            
            do{
                if (personTrackingResult !=null){
                    paginationToken = personTrackingResult.getNextToken();
                }
                
                personTrackingResult = rek.getPersonTracking(new GetPersonTrackingRequest()
                     .withJobId(startJobId)
                     .withNextToken(paginationToken)
                     .withSortBy(PersonTrackingSortBy.TIMESTAMP)
                     .withMaxResults(maxResults));
          
                VideoMetadata videoMetaData=personTrackingResult.getVideoMetadata();
                    
                System.out.println("Format: " + videoMetaData.getFormat());
                System.out.println("Codec: " + videoMetaData.getCodec());
                System.out.println("Duration: " + videoMetaData.getDurationMillis());
                System.out.println("FrameRate: " + videoMetaData.getFrameRate());
                    
                    
                //Show persons, confidence and detection times
                List<PersonDetection> detectedPersons= personTrackingResult.getPersons();
             
                for (PersonDetection detectedPerson: detectedPersons) { 
                    
                   long seconds=detectedPerson.getTimestamp()/1000;
                   System.out.print("Sec: " + Long.toString(seconds) + " ");
                   System.out.println("Person Identifier: "  + detectedPerson.getPerson().getIndex());
                      System.out.println();             
                }
            }  while (personTrackingResult !=null && personTrackingResult.getNextToken() != null);
            
        }

在函数 main 中，将以下行:


        StartLabelDetection(amzn-s3-demo-bucket, video);

        if (GetSQSMessageSuccess()==true)
        	GetLabelDetectionResults();

替换为:


        StartPersonDetection(amzn-s3-demo-bucket, video);

        if (GetSQSMessageSuccess()==true)
        	GetPersonDetectionResults();

Java V2

此代码取自 AWS 文档 SDK 示例 GitHub 存储库。请在此处查看完整示例。


import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.PersonDetection;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class VideoPersonDetection {
    private static String startJobId = "";

    public static void main(String[] args) {

        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startPersonLabels(rekClient, channel, bucket, video);
        getPersonDetectionResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startPersonLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartPersonTrackingRequest personTrackingRequest = StartPersonTrackingRequest.builder()
                    .jobTag("DetectingLabels")
                    .video(vidOb)
                    .notificationChannel(channel)
                    .build();

            StartPersonTrackingResponse labelDetectionResponse = rekClient.startPersonTracking(personTrackingRequest);
            startJobId = labelDetectionResponse.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getPersonDetectionResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetPersonTrackingResponse personTrackingResult = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (personTrackingResult != null)
                    paginationToken = personTrackingResult.nextToken();

                GetPersonTrackingRequest recognitionRequest = GetPersonTrackingRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds
                while (!finished) {

                    personTrackingResult = rekClient.getPersonTracking(recognitionRequest);
                    status = personTrackingResult.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = personTrackingResult.videoMetadata();

                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<PersonDetection> detectedPersons = personTrackingResult.persons();
                for (PersonDetection detectedPerson : detectedPersons) {
                    long seconds = detectedPerson.timestamp() / 1000;
                    System.out.print("Sec: " + seconds + " ");
                    System.out.println("Person Identifier: " + detectedPerson.person().index());
                    System.out.println();
                }

            } while (personTrackingResult != null && personTrackingResult.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Python


#Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)

    # ============== People pathing ===============  
    def StartPersonPathing(self):
        response=self.rek.start_person_tracking(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}},
            NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})

        self.startJobId=response['JobId']
        print('Start Job Id: ' + self.startJobId)
    
    def GetPersonPathingResults(self):
        maxResults = 10
        paginationToken = ''
        finished = False

        while finished == False:
            response = self.rek.get_person_tracking(JobId=self.startJobId,
                                            MaxResults=maxResults,
                                            NextToken=paginationToken)

            print('Codec: ' + response['VideoMetadata']['Codec'])
            print('Duration: ' + str(response['VideoMetadata']['DurationMillis']))
            print('Format: ' + response['VideoMetadata']['Format'])
            print('Frame rate: ' + str(response['VideoMetadata']['FrameRate']))
            print()

            for personDetection in response['Persons']:
                print('Index: ' + str(personDetection['Person']['Index']))
                print('Timestamp: ' + str(personDetection['Timestamp']))
                print()

            if 'NextToken' in response:
                paginationToken = response['NextToken']
            else:
                finished = True

在函数 main 中，将以下行:


    analyzer.StartLabelDetection()
    if analyzer.GetSQSMessageSuccess()==True:
        analyzer.GetLabelDetectionResults()

替换为:


    analyzer.StartPersonPathing()
    if analyzer.GetSQSMessageSuccess()==True:
        analyzer.GetPersonPathingResults()

CLI

运行以下 AWS CLI 命令开始人们在视频中寻找路径。


aws rekognition start-person-tracking --video "{"S3Object":{"Bucket":"amzn-s3-demo-bucket","Name":"video-name"}}" \ 
--notification-channel "{"SNSTopicArn":"topic-ARN","RoleArn":"role-ARN"}" \
--region region-name --profile profile-name

更新以下值：

将amzn-s3-demo-bucket和video-name更改为您在步骤 2 中指定的 Amazon S3 存储桶名称和文件名。
将 region-name 更改为您使用的 AWS 区域。
将创建 Rekognition 会话的行中的profile-name值替换为您的开发人员资料的名称。
将 topic-ARN 更改为您在配置 Amazon Rekognition Video 的步骤 3 中创建的 Amazon SNS 主题的 ARN。
将 role-ARN 更改为您在配置 Amazon Rekognition Video 的步骤 7 中创建的 IAM 服务角色的 ARN。

如果您在 Windows 设备上访问 CLI，请使用双引号代替单引号，并用反斜杠（即 \）对内部双引号进行转义，以解决可能遇到的任何解析器错误。有关示例，请参阅以下内容：


aws rekognition start-person-tracking --video "{\"S3Object\":{\"Bucket\":\"amzn-s3-demo-bucket\",\"Name\":\"video-name\"}}" 
--notification-channel "{\"SNSTopicArn\":\"topic-ARN\",\"RoleArn\":\"role-ARN\"}" \
--region region-name --profile profile-name

运行后续代码示例后，复制返回的 jobID 并将其提供给以下 GetPersonTracking 命令以获取结果，将 job-id-number 替换为您之前收到的 jobID：



aws rekognition get-person-tracking --job-id job-id-number

anchor anchor anchor anchor


       //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
       //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)

        //Persons========================================================================
        private static void StartPersonDetection(String bucket, String video) throws Exception{
            
            
            NotificationChannel channel= new NotificationChannel()
                    .withSNSTopicArn(snsTopicArn)
                    .withRoleArn(roleArn);
            
         StartPersonTrackingRequest req = new StartPersonTrackingRequest()
                 .withVideo(new Video()
                         .withS3Object(new S3Object()
                             .withBucket(bucket)
                             .withName(video)))
                 .withNotificationChannel(channel);
                                
             
            
         StartPersonTrackingResult startPersonDetectionResult = rek.startPersonTracking(req);
         startJobId=startPersonDetectionResult.getJobId();
            
        } 
        
        private static void GetPersonDetectionResults() throws Exception{
            int maxResults=10;
            String paginationToken=null;
            GetPersonTrackingResult personTrackingResult=null;
            
            do{
                if (personTrackingResult !=null){
                    paginationToken = personTrackingResult.getNextToken();
                }
                
                personTrackingResult = rek.getPersonTracking(new GetPersonTrackingRequest()
                     .withJobId(startJobId)
                     .withNextToken(paginationToken)
                     .withSortBy(PersonTrackingSortBy.TIMESTAMP)
                     .withMaxResults(maxResults));
          
                VideoMetadata videoMetaData=personTrackingResult.getVideoMetadata();
                    
                System.out.println("Format: " + videoMetaData.getFormat());
                System.out.println("Codec: " + videoMetaData.getCodec());
                System.out.println("Duration: " + videoMetaData.getDurationMillis());
                System.out.println("FrameRate: " + videoMetaData.getFrameRate());
                    
                    
                //Show persons, confidence and detection times
                List<PersonDetection> detectedPersons= personTrackingResult.getPersons();
             
                for (PersonDetection detectedPerson: detectedPersons) { 
                    
                   long seconds=detectedPerson.getTimestamp()/1000;
                   System.out.print("Sec: " + Long.toString(seconds) + " ");
                   System.out.println("Person Identifier: "  + detectedPerson.getPerson().getIndex());
                      System.out.println();             
                }
            }  while (personTrackingResult !=null && personTrackingResult.getNextToken() != null);
            
        }

在函数 main 中，将以下行:


        StartLabelDetection(amzn-s3-demo-bucket, video);

        if (GetSQSMessageSuccess()==true)
        	GetLabelDetectionResults();

替换为:


        StartPersonDetection(amzn-s3-demo-bucket, video);

        if (GetSQSMessageSuccess()==true)
        	GetPersonDetectionResults();

注意

如果您已经运行了除使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK) 之外的视频示例，则要替换的代码可能会有所不同。

运行该代码。将显示被跟踪人员的唯一标识符以及跟踪人物轨迹的时间（秒）。

GetPersonTracking 操作响应

GetPersonTracking 将返回一个数组 Persons，其中包括 PersonDetection 对象，这些对象包含有关所检测人物的详细信息以及跟踪人物轨迹的时间。

您可通过使用 SortBy 输入参数来为 Persons 排序。指定 TIMESTAMP 以按在视频中检测人物轨迹的时间为元素排序。指定 INDEX 以按在视频中跟踪的人员排序。在每组人物的结果中，元素是按人物轨迹跟踪的准确性的置信度的降序顺序排序的。默认情况下，Persons 将按 TIMESTAMP 返回/排序。以下示例是 GetPersonDetection 的 JSON 响应。结果按在视频中跟踪人物轨迹的时间（播放视频的毫秒数）排序。在响应中，请注意以下内容：

人员信息 – PersonDetection 数组元素包含有关检测到的人员的信息。例如，检测到人员的时间（Timestamp）、检测到时人员在视频帧中的位置（BoundingBox）以及 Amazon Rekognition Video 对正确检测到人员的置信度（Confidence）。

跟踪人物轨迹的每个时间戳不会返回面部特征。而且，在被跟踪人员的身体可能不可见的某些情况下，仅返回其脸部位置。
分页信息 – 此示例显示一页的人员检测信息。您可以在 GetPersonTracking 的 MaxResults 输入参数中指定要返回的人员元素数量。如果存在的结果的数量超过了 MaxResults，则 GetPersonTracking 会返回一个令牌 (NextToken)，用于获取下一页的结果。有关更多信息，请参阅获取 Amazon Rekognition Video 分析结果。
索引 – 用于在整个视频中识别人物的唯一标识符。
视频信息 – 此响应包含有关由 GetPersonDetection 返回的每页信息中的视频格式（VideoMetadata）的信息。


{
    "JobStatus": "SUCCEEDED",
    "NextToken": "AcDymG0fSSoaI6+BBYpka5wVlqttysSPP8VvWcujMDluj1QpFo/vf+mrMoqBGk8eUEiFlllR6g==",
    "Persons": [
        {
            "Person": {
                "BoundingBox": {
                    "Height": 0.8787037134170532,
                    "Left": 0.00572916679084301,
                    "Top": 0.12129629403352737,
                    "Width": 0.21666666865348816
                },
                "Face": {
                    "BoundingBox": {
                        "Height": 0.20000000298023224,
                        "Left": 0.029999999329447746,
                        "Top": 0.2199999988079071,
                        "Width": 0.11249999701976776
                    },
                    "Confidence": 99.85971069335938,
                    "Landmarks": [
                        {
                            "Type": "eyeLeft",
                            "X": 0.06842322647571564,
                            "Y": 0.3010137975215912
                        },
                        {
                            "Type": "eyeRight",
                            "X": 0.10543643683195114,
                            "Y": 0.29697132110595703
                        },
                        {
                            "Type": "nose",
                            "X": 0.09569807350635529,
                            "Y": 0.33701086044311523
                        },
                        {
                            "Type": "mouthLeft",
                            "X": 0.0732642263174057,
                            "Y": 0.3757539987564087
                        },
                        {
                            "Type": "mouthRight",
                            "X": 0.10589495301246643,
                            "Y": 0.3722417950630188
                        }
                    ],
                    "Pose": {
                        "Pitch": -0.5589138865470886,
                        "Roll": -5.1093974113464355,
                        "Yaw": 18.69594955444336
                    },
                    "Quality": {
                        "Brightness": 43.052337646484375,
                        "Sharpness": 99.68138885498047
                    }
                },
                "Index": 0
            },
            "Timestamp": 0
        },
        {
            "Person": {
                "BoundingBox": {
                    "Height": 0.9074074029922485,
                    "Left": 0.24791666865348816,
                    "Top": 0.09259258955717087,
                    "Width": 0.375
                },
                "Face": {
                    "BoundingBox": {
                        "Height": 0.23000000417232513,
                        "Left": 0.42500001192092896,
                        "Top": 0.16333332657814026,
                        "Width": 0.12937499582767487
                    },
                    "Confidence": 99.97504425048828,
                    "Landmarks": [
                        {
                            "Type": "eyeLeft",
                            "X": 0.46415066719055176,
                            "Y": 0.2572723925113678
                        },
                        {
                            "Type": "eyeRight",
                            "X": 0.5068183541297913,
                            "Y": 0.23705792427062988
                        },
                        {
                            "Type": "nose",
                            "X": 0.49765899777412415,
                            "Y": 0.28383663296699524
                        },
                        {
                            "Type": "mouthLeft",
                            "X": 0.487221896648407,
                            "Y": 0.3452930748462677
                        },
                        {
                            "Type": "mouthRight",
                            "X": 0.5142884850502014,
                            "Y": 0.33167609572410583
                        }
                    ],
                    "Pose": {
                        "Pitch": 15.966927528381348,
                        "Roll": -15.547388076782227,
                        "Yaw": 11.34195613861084
                    },
                    "Quality": {
                        "Brightness": 44.80223083496094,
                        "Sharpness": 99.95819854736328
                    }
                },
                "Index": 1
            },
            "Timestamp": 0
        }.....

    ],
    "VideoMetadata": {
        "Codec": "h264",
        "DurationMillis": 67301,
        "FileExtension": "mp4",
        "Format": "QuickTime / MOV",
        "FrameHeight": 1080,
        "FrameRate": 29.970029830932617,
        "FrameWidth": 1920
    }
}