Vision Tracking With Dwell Time for Accurate Occupancy Counting
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Solution Overview
Problem
Conventional vision systems fail to provide accurate object detection and counting due to sub-par detection algorithms and unrecognized movements, such as people moving in unorthodox trajectories or having prolonged dwell times near entry/exit points, leading to inaccurate occupancy counts.
Innovation Solution
A computer vision-based system that tracks movements and calculates dwell time to differentiate between intended entries/ exits by comparing detected paths with target paths and dwell times, adjusting occupancy counts accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection algorithms are used to track occupancy, then the system is simple to implement, but the occupancy count accuracy deteriorates due to unrecognized movements and sub-par detection
Solution Approach 1:
The detection process is segmented into multiple stages: initial object detection, trajectory analysis, dwell time calculation, and path comparison. Each stage processes specific aspects of movement independently, improving overall accuracy without requiring a single complex algorithm to handle all aspects simultaneously.
Solution Approach 2:
The system transitions from simple 2D frame-based detection to 3D trajectory analysis by incorporating temporal dimension. It tracks objects across multiple frames to generate movement paths, adding the time dimension to distinguish between intentional entry/exit and incidental movements near access points.
2Measurement precision
If the system counts all detected objects crossing a boundary, then the counting process is simple, but the occupancy count accuracy deteriorates due to unorthodox trajectories and prolonged dwell times
Solution Approach 1:
The system performs preliminary trajectory analysis and dwell time calculation before finalizing the occupancy count. By pre-processing movement patterns and identifying anomalous trajectories early in the detection pipeline, it avoids unnecessary counting operations and reduces false positives before they affect the final count.
Solution Approach 2:
The system introduces an intermediary verification layer between detection and counting. This layer analyzes movement patterns, calculates dwell times, and compares trajectories against expected paths, acting as a mediator that filters out false detections before they increment the occupancy count.
3Measurement precision
If the system analyzes path and dwell time to differentiate traffic types, then the occupancy count accuracy improves, but the computational complexity increases
Solution Approach 1:
The system applies different analysis depths to different objects based on their proximity to access points and movement characteristics. Objects near access points with anomalous trajectories receive full path and dwell time analysis, while objects with clear entry/exit patterns receive minimal processing, optimizing computational resource allocation.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as dwell time thresholds and path deviation tolerances based on environmental context and traffic patterns. By adapting these parameters rather than using fixed values, the system reduces unnecessary computational overhead while maintaining high accuracy across varying conditions.
Data Source
AI summary
Disclosed herein are apparatuses and methods for tracking occupancy count in an environment based on detected movements and dwell time. An implementation may comprise detecting a person in a plurality of image frames captured by a camera. The implementation may comprise tracking a movement of the person and determining that the person has crossed a boundary within the plurality of image frames. The implementation may comprise identifying a path of the person based on the tracked movement and may comprise calculating a dwell time in response to determining that a difference between the identified path and at least one target path associated with entering or exiting the environment exceeds a threshold difference. The implementation may comprise comparing the dwell time to a target dwell time, and updating and storing the occupancy count of the environment in response to determining that the dwell time is less than the target dwell time.