Hierarchical Training for Video-Based Vehicle Detection
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Solution Overview
Problem
Current methods for detecting vehicle occupancy in parking spaces, such as sensor-based systems, are costly and inefficient, especially in multi-space parking configurations, where they struggle to accurately detect vehicles without pre-defined boundaries, and video-based solutions require manual training and are not scalable for large deployments.
Innovation Solution
A video-based method using a sliding window-based space search and a hierarchical training approach to automate the deployment of vehicle detection systems, where a generic classifier is used to collect high-confidence samples and train a site-specific classifier, reducing manual intervention and improving detection accuracy across varying camera configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based methods are used for vehicle detection, then detection accuracy is improved, but installation and maintenance costs increase significantly
Solution Approach 1:
The patent replaces sensor-based detection systems with a video-based computer vision system. Instead of using physical sensors (magnetometers, ultrasonic sensors) embedded in the ground, the system uses video cameras to capture images and automated algorithms to detect vehicles. This substitution eliminates the need for expensive sensor installation and maintenance while achieving comparable detection accuracy through image processing and pattern recognition.
Solution Approach 2:
The patent creates a virtual model of the parking area by capturing real-world scenes through video cameras. The system processes video frames to generate digital representations of parking space occupancy, replacing the need for physical sensors in each parking space. This copying approach allows one camera to monitor multiple spaces, reducing overall system cost and complexity.
2Ease of manufacture
If video-based solutions are used for multi-space parking configurations, then cost is reduced, but manual training requirements increase
Solution Approach 1:
The patent implements an automated training system where the video detection system trains itself using video data from the parking areas. The system automatically collects training samples, identifies vehicle and non-vehicle regions, and iteratively improves its detection algorithms without requiring manual annotation or configuration. This self-service approach eliminates the time-consuming manual training process while maintaining low system costs.
Solution Approach 2:
The patent performs preliminary automated training by collecting and processing video samples before full deployment. The system pre-trains its detection models using available video data, creating a ready-to-use detection system that can be quickly deployed across multiple parking areas without requiring site-specific manual training for each location.
3Ease of manufacture
If a single video camera monitors multiple parking spaces, then device quantity is reduced, but detection complexity increases
Solution Approach 1:
The patent divides the video image into multiple regions corresponding to individual parking spaces. The detection algorithm processes each region separately, identifying vehicles within specific bounding boxes rather than analyzing the entire image as one unit. This segmentation approach simplifies the detection task for each space while allowing a single camera to monitor multiple spaces effectively.
Solution Approach 2:
The patent transitions from monitoring individual parking spaces in isolation to a multi-dimensional approach where one camera captures a broader view encompassing multiple spaces. The system adds the dimension of spatial relationships by analyzing the relative positions of vehicles across different parking spaces in a single video frame, enabling efficient multi-space monitoring without increasing device count.
Data Source
AI summary
Disclosed are methods and systems for detecting one or more vehicles in video captured from a deployed video camera directed at a parking region. According to one exemplary embodiment, disclosed is a method of training a deployed classifier associated with the video camera, where a generic classifier is initially used to obtain high confidence training samples from the video camera, the high confidence training samples subsequently used to train the deployed classifier.