#LEARNING RESOURCES TIME TRACKER TIMER CODE#
We recommend running the source code in the us-east-1 Region.Create an AWS account or use an existing AWS account.The following diagram illustrates the architecture in each step.īefore getting started, complete the following prerequisites: Deploy the trained ByteTrack model with different deployment options depending on your use case: real-time processing, asynchronous, or batch prediction.Train a ByteTrack model and tune hyperparameters on a custom dataset.Set up the resources for ML code development and execution. Label the dataset for tracking, with a bounding box on each object (for example, pedestrian, car, and so on).
Our solution consists of the following high-level steps:
#LEARNING RESOURCES TIME TRACKER TIMER HOW TO#
In this post, we show how to deploy a tracking model with different deployment options, so that you can choose the suitable deployment method in your own use case. SageMaker provides plenty of options for model deployment, such as real-time inference, serverless inference, and asynchronous inference. For more information about deciding on the right level of engagement with containers, refer to Using Docker containers with SageMaker. Additionally, custom algorithms such as ByteTrack can also be supported via custom-built Docker container images. SageMaker provides several built-in algorithms and container images that you can use to accelerate training and deployment of ML models.
SageMaker is a fully managed service that provides every developer and data scientist with the ability to prepare, build, train, and deploy machine learning (ML) models quickly. We also provide the code sample on GitHub, which uses SageMaker for labeling, building, training, and inference.
When applying a MOT solution in real-world cases, you need to train or fine-tune a MOT model on a custom dataset.
In the post Train and deploy a FairMOT model with Amazon SageMaker, we demonstrated how to train and deploy a FairMOT model with Amazon SageMaker on the MOT challenge datasets. For example, FairMOT achieved an improvement of 1.3% on MOTA ( FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking), which is one of the main metrics in the MOT task when applying BYTE in data association. The experiments showed improvements compared to the vanilla tracker algorithms. The BYTE association strategy can also be used in other Re-ID based trackers, such as FairMOT. Rather than only keep the high score detection boxes, it also keeps the low score detection boxes, which can help recover unmatched tracklets with these low score detection boxes when occlusion, motion blur, or size changing occurs. In ByteTrack, the author proposed a simple, effective, and generic data association method (referred to as BYTE) for detection box and tracklet matching. Since its introduction in 2021, ByteTrack remains to be one of best performing methods on various benchmark datasets, among the latest model developments in MOT application. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. The demand for multi-object tracking (MOT) in video analysis has increased significantly in many industries, such as live sports, manufacturing, and traffic monitoring.
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