PeopleNet AMR Model Card

Model Overview

The Peoplenet Autonomous Mobile Robot (AMR) model described in this card detect one or more physical objects from people category within an image, predicts a box around each object, and returns a masked image aggregating all boxes around detected objects. The model is based on PeopleNet, and optimized for people detection on RealSense applications running at 480x640 image resolution.

Model Architecture

This model is based on NVIDIA DetectNet_v2 detector with ResNet34 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.

The raw normalized bounding-box and confidence detections are the postprocessed to a masked image, where all pixels within bounding boxes are masked with 1.

Training

This model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained without regularization.

Training Data

PeopleNet AMR model was trained on a proprietary dataset with more than 3 million images and more than 8 million objects for person class. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV). Approximately two thirds of the training data consisted of images captured in a indoor and outdoor environments that are from a horizontal view point. For this case, the camera is typically set up at approximately 2 to 5 feet height, 90-degree angle and has wide field-of-view. We have also added approximately 45 thousand images with low-density scenes from a robot's point of view to improve the performance for use-cases where person object detection is needed at low heights.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Category	Number of Images	Number of Persons	Number of Bags	Number of Faces
Natural	1920657	6592311	1811658	3032396
-- Robotics Subset	43076	160806	40413	24109
Rotated	1020163	1800844	291754	963805
Total	3028550	8553961	2143825	4020310

Training Data Ground-truth Labeling Guidelines

The training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA PeopleNet AMR model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.

PeopleNet AMR project labelling guidelines:

All objects that fall under person in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.
If a person is carrying an object please mark the bounding-box to include the carried object as long as it doesn’t affect the silhouette of the person. For example, exclude a rolling bag if they are pulling it behind them and are distinctly visible as separate object. But include a backpack, purse etc. that do not alter the silhouette of the pedestrian significantly.
Occlusion: For partially occluded objects that do not belong a person class and are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.
Occlusion for person class: If an occluded person’s head and shoulders are visible and the visible height is approximately 20% or more, then these objects are marked by the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Occlusion guidelines in item 3 above.
Truncation: For an object other than a person that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.
Truncation for person class: If a truncated person’s head and shoulders are visible and the visible height is approximately 20% or more mark the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Truncation guidelines in item 5 above.
Each frame is not required to have an object.

Performance

Evaluation Data

The inference performance of PeopleNet AMR v0.1 model was measured against more than 15000 proprietary images across a variety of environments. The frames are high resolution images 1920x1080 pixels resized to 960x544 pixels before passing to the PeopleNet AMR detection model.

Methodology and KPI

The true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. The FP16 Model is evaluated based on precision, recall and accuracy.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Content	Precision	Recall	Accuracy
Generic	98.75	94.71	85.47
Office	94.24	78.05	74.50
Robotics	89.01	83.14	76.39
Extended Hands	97.00	89.84	87.41
Extended-hands $(\\mathrm{IOU}>0.8)$	91.50	84.76	78.57
People $(\\mathrm{IOU}>0.8)$	82.56	77.62	66.69
Robotics $(\\mathrm{IOU}>0.8)$	84.81	80.28	70.91

How to use this model

This model needs to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with TensorRT.

The primary use case intended for this model is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing.

Input

RGB Image of dimensions: 480 x 640 X 3 (H x W x C). Channel Ordering of the Input: NHWC, where N = Batch Size, H = Height of images (480), W = Width of the images (640) , C = number of channels (3). Input scale: 1/255.0 Mean subtraction: None

Output

Masked image : 480 x 640 x 1 (H x W x C) where pixels indicating detected people are 1. and 0. otherwise. Channel Ordering of the Output: NHWC, where N = Batch Size, H = Height of images (480), W = Width of the images (640) , C = number of channels (1). Input scale: 0. / 1.

Limitations

Very Small Objects

NVIDIA PeopleNet AMR model were trained to detect objects larger than 10x10 pixels. Therefore it may not be able to detect objects that are smaller than 10x10 pixels.

Occluded Objects

When objects are occluded or truncated such that less than 20% of the object is visible, they may not be detected by the PeopleNet AMR model. For people class objects, the model will detect occluded people as long as head and shoulders are visible. However if the person’s head and/or shoulders are not visible, the object might not be detected unless more than 60% of the person is visible.

Dark-lighting, Monochrome or Infrared Camera Images

The PeopleNet AMR model were trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.

Warped and Blurry Images

The PeopleNet AMR models were not trained on fish-eye lense cameras. Therefore, the models may not perform well for warped images and images that have a lot of blur.

Face and Bag class

Although bag and face class are included in the model, the accuracy of these classes will be much lower than people class. Some re-training will be required on these classes to improve accuracy.

References

Citations

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)

License

License to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Ethical Considerations

NVIDIA PeopleNet AMR model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.

Training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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Used in nvblox multi-camera optimization.","## PeopleNet AMR Model Card\n\n### Model Overview\nThe Peoplenet Autonomous Mobile Robot (AMR) model described in this card detect one or more physical objects from people category within an image, predicts a box around each object, and returns a masked image aggregating all boxes around detected objects. The model is based on [PeopleNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet_amr), and optimized for people detection on RealSense applications running at 480x640 image resolution.\n\n### Model Architecture\nThis model is based on NVIDIA DetectNet_v2 detector with ResNet34 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.\n\nThe raw normalized bounding-box and confidence detections are the postprocessed to a masked image, where all pixels within bounding boxes are masked with 1.\n\n### Training\nThis model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained without regularization.\n\n#### Training Data\nPeopleNet AMR model was trained on a proprietary dataset with more than 3 million images and more than 8 million objects for person class. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV). Approximately two thirds of the training data consisted of images captured in a indoor and outdoor environments that are from a horizontal view point. For this case, the camera is typically set up at approximately 2 to 5 feet height, 90-degree angle and has wide field-of-view. We have also added approximately 45 thousand images with low-density scenes from a robot's point of view to improve the performance for use-cases where person object detection is needed at low heights.\n\n| Category | Number of Images | Number of Persons | Number of Bags | Number of Faces |\n| :--- | :--- | :--- | :--- | :--- |\n| Natural | 1920657 | 6592311 | 1811658 | 3032396 |\n| -- Robotics Subset | 43076 | 160806 | 40413 | 24109 |\n| Rotated | 1020163 | 1800844 | 291754 | 963805 |\n| Total | 3028550 | 8553961 | 2143825 | 4020310 |\n\n#### Training Data Ground-truth Labeling Guidelines\nThe training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA PeopleNet AMR model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.\n\n#### PeopleNet AMR project labelling guidelines:\n\n1. All objects that fall under person in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.\n2. If a person is carrying an object please mark the bounding-box to include the carried object as long as it doesn’t affect the silhouette of the person. For example, exclude a rolling bag if they are pulling it behind them and are distinctly visible as separate object. But include a backpack, purse etc. that do not alter the silhouette of the pedestrian significantly.\n3. Occlusion: For partially occluded objects that do not belong a person class and are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.\n4. Occlusion for person class: If an occluded person’s head and shoulders are visible and the visible height is approximately 20% or more, then these objects are marked by the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Occlusion guidelines in item 3 above.\n5. Truncation: For an object other than a person that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.\n6. Truncation for person class: If a truncated person’s head and shoulders are visible and the visible height is approximately 20% or more mark the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Truncation guidelines in item 5 above.\n7. Each frame is not required to have an object.\n\n### Performance\n\n#### Evaluation Data\nThe inference performance of PeopleNet AMR v0.1 model was measured against more than 15000 proprietary images across a variety of environments. The frames are high resolution images 1920x1080 pixels resized to 960x544 pixels before passing to the PeopleNet AMR detection model.\n\n#### Methodology and KPI\nThe true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. The FP16 Model is evaluated based on precision, recall and accuracy.\n\n| Content | Precision | Recall | Accuracy |\n| :--- | :--- | :--- | :--- |\n| Generic | 98.75 | 94.71 | 85.47 |\n| Office | 94.24 | 78.05 | 74.50 |\n| Robotics | 89.01 | 83.14 | 76.39 |\n| Extended Hands | 97.00 | 89.84 | 87.41 |\n| Extended-hands $(\\mathrm{IOU}>0.8)$ | 91.50 | 84.76 | 78.57 |\n| People $(\\mathrm{IOU}>0.8)$ | 82.56 | 77.62 | 66.69 |\n| Robotics $(\\mathrm{IOU}>0.8)$ | 84.81 | 80.28 | 70.91 |\n\n### How to use this model\nThis model needs to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case intended for this model is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing.\n\n#### Input\nRGB Image of dimensions: 480 x 640 X 3 (H x W x C). Channel Ordering of the Input: NHWC, where N = Batch Size, H = Height of images (480), W = Width of the images (640) , C = number of channels (3). Input scale: 1/255.0 Mean subtraction: None\n\n#### Output\nMasked image : 480 x 640 x 1 (H x W x C) where pixels indicating detected people are 1. and 0. otherwise. Channel Ordering of the Output: NHWC, where N = Batch Size, H = Height of images (480), W = Width of the images (640) , C = number of channels (1). Input scale: 0. / 1.\n\n### Limitations\n#### Very Small Objects\nNVIDIA PeopleNet AMR model were trained to detect objects larger than 10x10 pixels. Therefore it may not be able to detect objects that are smaller than 10x10 pixels.\n\n#### Occluded Objects\nWhen objects are occluded or truncated such that less than 20% of the object is visible, they may not be detected by the PeopleNet AMR model. For people class objects, the model will detect occluded people as long as head and shoulders are visible. However if the person’s head and/or shoulders are not visible, the object might not be detected unless more than 60% of the person is visible.\n\n#### Dark-lighting, Monochrome or Infrared Camera Images\nThe PeopleNet AMR model were trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.\n\n#### Warped and Blurry Images\nThe PeopleNet AMR models were not trained on fish-eye lense cameras. Therefore, the models may not perform well for warped images and images that have a lot of blur.\n\n#### Face and Bag class\nAlthough bag and face class are included in the model, the accuracy of these classes will be much lower than people class. Some re-training will be required on these classes to improve accuracy.\n\n### References\n#### Citations\n- Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)\n- Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\n\n### License\nLicense to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://developer.nvidia.com/deep-learning-models-license-agreement).\n\n### Ethical Considerations\nNVIDIA PeopleNet AMR model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.\n\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","PeopleNet AMR",["D",1733260438603],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/isaac/models/optimized_peoplenet_amr","rawImageUrl","https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ngc/icons/m48-robot-256px-grn.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Participation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | Gender, Skin Color |\n| Measures taken to mitigate against unwanted bias: | Developed customized dataset that included video from persons of different skin colors in different geographic settings. Unwanted bias is mitigated by the collection of data in different urban areas at different times of the day during typical working hours and outside of typical working times to get a diversity of races and skin tones. |","explainability","| Field | Response |\n|---------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Intended Application(s) & Domain(s): | Detecting persons. |\n| Model Type: | Object Detection |\n| Intended Users: | Developers working in robotics applications to help autonomous machines navigate floor space. |\n| Output: | Bounding Box(es), Category Label(s), Confidence Score(s) |\n| Describe how the model works: | Detects and labels persons within an image and returns bounding box coordinates, labels, and confidence scores for each category. |\n| Technical Limitations: | Model accuracy may be reduced in low-light, low contrast lighting conditions, with warped images, objects positioning relatively close to or far away from the camera frame (smaller than 20x20 pixels), and with objects occluded (less than 40% of the object is visible) or truncated. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy, Intersection over Union(IoU), Precision, Recall |\n| Potential Known Risks: | Not recommended for life-critical use cases |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n|-----------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | Developed customized dataset that included video from persons of different skin colors in different geographic settings. Unwanted bias is mitigated by the collection of data in different urban areas at different times of the day during typical working hours and outside of typical working times to get a diversity of races and skin tones. |\n| Was consent obtained for any PII used? | Yes |\n| How often is dataset reviewed? | Before Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Yes |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | Yes, PII was collected by NVIDIA. |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\n| Applicable NVIDIA Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/]( https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n|------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Model Application(s): | Object Detection |\n| Describe the life-critical application (if present). | Potential for life-critical impact in detecting the presence of people in floor space. |\n| Use Case Restrictions: | Should not be used in \"Critical Applications;\" (reference)[https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/] |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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