Occupancy Analytics with Computer Vision

Computer vision technology facilitates real-time video analytics to derive insights from complex environments, such as parking lots, by analyzing video feeds to identify patterns in space usage and occupancy. This process involves collecting data, training a model, and running the model to calculate metrics and generate visualizations that answer questions about space utilization, such as vacancy rates and peak usage times. The guide details steps for creating and training a custom computer vision model using Supervision and Roboflow tools, employing techniques like Slicing Aided Hyper Inference (SAHI) to improve model performance for detecting multiple small objects. Once trained, the model processes video frames to generate occupancy metrics, heatmaps, and visualizations that highlight busy and underutilized areas. The information gathered can be used to optimize operations, reduce congestion, and make data-driven decisions about space management. This methodology is adaptable to various use cases beyond parking lots, demonstrating the broad applicability of computer vision in occupancy analytics.

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