Vehicle Detection via Rotated ROI and 2D Sliding Window
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Solution Overview
Problem
Conventional methods for detecting vehicle occupancy in parking spaces, such as sensor-based solutions, are costly and inefficient for multi-space parking configurations, and video-based solutions face computational challenges in real-time processing due to 4-D sliding window searches.
Innovation Solution
A computationally efficient video-based vehicle detection method and system that rotates the Region of Interest (ROI) to perform a 2-D sliding window search within a fixed camera's field of view, reducing the search space and enhancing processing efficiency without compromising accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 4-D sliding window search is performed for vehicle detection in video frames, then comprehensive vehicle localization is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the vehicle detection process into distinct phases: training phase where a classifier is learned from labeled data, and detection phase where the pre-trained classifier is applied to video frames. This segmentation allows the computationally intensive learning process to be performed once offline, while real-time detection uses the lightweight classifier, thereby reducing online computational complexity while maintaining localization accuracy
Solution Approach 2:
The patent performs preliminary action by training the vehicle classifier offline using labeled training images before actual vehicle detection. The training process, which involves learning complex features and decision boundaries, is completed in advance so that during real-time operation, only the classification step needs to be executed, significantly reducing processing time and computational load during deployment
2Measurement precision
If sensor-based methods are used for parking space monitoring, then accurate vehicle detection is achieved, but installation and maintenance costs increase
Solution Approach 1:
The patent uses video camera images as a visual copy or representation of the physical parking space occupancy state, replacing the need for physical sensors embedded in the ground. The image-based detection system captures optical information about vehicle presence, providing a cost-effective alternative to expensive sensor infrastructure while maintaining detection accuracy
Solution Approach 2:
The patent replaces the mechanical sensor-based detection system with an optical/image-based system. Instead of using physical sensors that require installation in the ground and periodic maintenance, the system uses video cameras to capture images and a computer vision classifier to detect vehicles, eliminating mechanical components and reducing maintenance requirements
3Ease of manufacture
If video-based solutions are used for multi-space parking configurations, then cost is reduced, but computational efficiency decreases due to complex search spaces
Solution Approach 1:
The patent changes the parameters of the detection approach by using a pre-trained classifier with fixed decision boundaries instead of performing exhaustive sliding window searches with multiple scales and orientations during real-time operation. This parameter change allows the system to process video frames efficiently while maintaining accurate vehicle detection in multi-space parking configurations
Data Source
AI summary
Provided is a method and system for efficient localization in still images. According to one exemplary method, a sliding window-based 2-D (Dimensional) space search is performed to detect a parked vehicle in a video frame acquired from a fixed parking occupancy video camera including a field of view associated with a parking region.