People pathing - Amazon Rekognition

People pathing

Amazon Rekognition Video can create a track of the path people take in videos and provide information such as:

  • The location of the person in the video frame at the time their path is tracked.

  • Facial landmarks such as the position of the left eye, when detected.

Amazon Rekognition Video people pathing in stored videos is an asynchronous operation. To start the pathing of people in videos call StartPersonTracking. Amazon Rekognition Video publishes the completion status of the video analysis to an Amazon Simple Notification Service topic. If the video analysis is successful, call GetPersonTracking to get results of the video analysis. For more information about calling Amazon Rekognition Video API operations, see Calling Amazon Rekognition Video operations.

The following procedure shows how to track the path of people through a video stored in an Amazon S3 bucket. The example expands on the code in Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK) which uses an Amazon Simple Queue Service queue to get the completion status of a video analysis request.

To detect people in a video stored in an Amazon S3 bucket (SDK)
  1. Perform Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK).

  2. Add the following code to the class VideoDetect that you created in step 1.

    //Copyright 2018, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see //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); }

    In the function main, replace the lines:

    StartLabelDetection(bucket, video); if (GetSQSMessageSuccess()==true) GetLabelDetectionResults();


    StartPersonDetection(bucket, video); if (GetSQSMessageSuccess()==true) GetPersonDetectionResults();
    Java V2

    This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example here.

    import; import; import; import; import; import; import; import; import; import; import; import; 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: * * */ 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); } } }
    #Copyright 2018, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see # ============== People pathing =============== def StartPersonPathing(self): response=self.rek.start_person_tracking(Video={'S3Object': {'Bucket': self.bucket, 'Name':}}, 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

    In the function main, replace the lines:

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


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

    Run the following AWS CLI command to start people pathing in a video.

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

    Update the following values:

    • Change bucket-name and video-name to the Amazon S3 bucket name and file name that you specified in step 2.

    • Change region-name to the AWS region that you're using.

    • Replace the value of profile-name in the line that creates the Rekognition session with the name of your developer profile.

    • Change topic-ARN to the ARN of the Amazon SNS topic you created in step 3 of Configuring Amazon Rekognition Video.

    • Change role-ARN to the ARN of the IAM service role you created in step 7 of Configuring Amazon Rekognition Video.

    If you are accessing the CLI on a Windows device, use double quotes instead of single quotes and escape the inner double quotes by backslash (i.e. \) to address any parser errors you may encounter. For an example, see below:

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

    After running the proceeding code example, copy down the returned jobID and provide it to the following GetPersonTracking command below to get your results, replacing job-id-number with the jobID you previously received:

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

    If you've already run a video example other than Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK), the code to replace might be different.

  3. Run the code. The unique identifiers for tracked people are shown along with the time, in seconds, the people's paths were tracked.

GetPersonTracking operation response

GetPersonTracking returns an array, Persons, of PersonDetection objects which contain details about people detected in the video and when their paths are tracked.

You can sort Persons by using the SortBy input parameter. Specify TIMESTAMP to sort the elements by the time people's paths are tracked in the video. Specify INDEX to sort by people tracked in the video. Within each set of results for a person, the elements are sorted by descending confidence in the accuracy of the path tracking. By default, Persons is returned sorted by TIMESTAMP. The following example is the JSON response from GetPersonDetection. The results are sorted by the time, in milliseconds since the start of the video, that people's paths are tracked in the video. In the response, note the following:

  • Person information – The PersonDetection array element contains information about the detected person. For example, the time the person was detected (Timestamp), the position of the person in the video frame at the time they were detected (BoundingBox), and how confident Amazon Rekognition Video is that the person has been correctly detected (Confidence).

    Facial features are not returned at every timestamp for which the person's path is tracked. Furthermore, in some circumstances a tracked person's body might not be visible, in which case only their face location is returned.

  • Paging information – The example shows one page of person detection information. You can specify how many person elements to return in the MaxResults input parameter for GetPersonTracking. If more results than MaxResults exist, GetPersonTracking returns a token (NextToken) used to get the next page of results. For more information, see Getting Amazon Rekognition Video analysis results.

  • Index – A unique identifier for identifying the person throughout the video.

  • Video information – The response includes information about the video format (VideoMetadata) in each page of information returned by GetPersonDetection.

{ "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 } }