You work for a company that captures live video footage of checkout areas in their retail stores. You need to use the live video footage to build a model to detect the number of customers waiting for service in near real time. You want to implement a solution quickly and with minimal effort. How should you build the model?
To detect the number of customers waiting for service in near real time, the Vertex AI Vision Occupancy Analytics model is the most suitable choice. This pre-trained model is specifically designed to count people in live video streams, which aligns perfectly with the task at hand. It offers near real-time performance with minimal configuration, making it ideal for quick implementation and reducing the effort required compared to custom training models.
My Answer: A: Vertex AI Vision Occupancy Analytics is a pre-trained model specifically designed to count people in live video streams. This removes the need for expensive and time-consuming data labeling and training, making it ideal for quick implementation. ****Vertex AI Vision Person/Vehicle Detector model detects individual people and vehicles, not specifically focusing on occupancy counting. It would require further processing to estimate the number of waiting customers. Option C and D requires labeling data and training, which adds effort and time. https://cloud.google.com/vision-ai/docs/overview
A. Use the Vertex AI Vision Occupancy Analytics model: This is a pre-built model specifically designed for analyzing occupancy in videos. It's ideal for this scenario as it requires minimal configuration and can likely be deployed quickly.
C. Train an AutoML object detection model: While this could be a good solution in the long run, training a custom model requires creating an annotated dataset and takes time. D. Seq2Seq+ object detection model: This is an overly complex approach for this task. Seq2Seq models are used for sequence-to-sequence prediction tasks and are not necessary here.
https://codelabs.developers.google.com/vertex-ai-vision-queue-detection#0
A. Use the Vertex AI Vision Occupancy Analytics model. Here's why: Pre-trained and optimized: Occupancy Analytics is a pre-trained and optimized model specifically designed for counting people in video footage, aligning perfectly with your task. This eliminates the need for extensive data collection, annotation, and training, saving time and effort. Near real-time performance: The model is designed for low latency and near real-time inference, providing results quickly with minimal delay, important for live video analysis. Minimal configuration: Compared to training your own model, this option requires minimal configuration within the Vertex AI console, allowing for a quicker setup and deployment.
All you need is counting the number of customers in the video stream. I would say no need to have the extra functionalities of occupancy analytics, person/vehicle is enough for this use case. https://cloud.google.com/vision-ai/docs/person-vehicle-model
https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/vehicle-detector Occupancy analytics has other features too like zone detection, dwell time, and more, which is not needed in this scenario.