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.
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.
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.
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.
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 |
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.
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.
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.
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 |
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.
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
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.
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.
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.
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.
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.
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.
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.
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.