Cascade Classifier Depth Object Detection
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Solution Overview
Problem
Existing gesture recognition technologies face challenges in accurately detecting hand regions due to sensitivity to lighting conditions and the requirement for the hand to be in the foremost position, with methods using color image information being vulnerable to environmental changes and those using depth information struggling to distinguish arm and hand regions.
Innovation Solution
A system and method utilizing a cascade-based boosting classifier and depth difference characteristics to classify and detect object regions in real-time from depth images, comprising a depth image receiver, object characteristic extractor, and object detector with cascade-connected weak classifiers trained on positive and negative training images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If color image information is used for hand detection, then detection speed is improved, but detection reliability deteriorates due to sensitivity to lighting conditions
Solution Approach 1:
The patent segments the detection process into multiple stages using a cascade classifier structure. The detection is divided into sequential steps where simple weak classifiers are applied in sequence, with each stage filtering out non-hand regions more efficiently. This segmentation allows faster processing while maintaining reliability through progressive refinement.
Solution Approach 2:
The patent combines multiple types of features (color information, depth information, gradient information) to create a composite detection approach. By integrating heterogeneous feature types in the cascade classifier, the system achieves both fast detection speed and high reliability, overcoming the limitations of using single feature types alone.
2Reliability
If depth image information is used for hand detection, then detection reliability is improved, but detection capability deteriorates when hand is not in foremost position
Solution Approach 1:
The patent incorporates depth information as an additional dimension in the feature space, creating a multi-dimensional detection approach. By adding the depth dimension to traditional color and gradient features, the system can reliably detect hands at various distances from the camera, not just when they are in the foremost position, thus improving adaptability while maintaining reliability.
3Measurement precision
If cascade-based boosting classifier is used, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The cascade classifier segments the detection task into multiple sequential stages, each performing a simplified classification. This segmentation allows the system to achieve high overall accuracy through cumulative filtering while keeping individual stage complexity low, enabling real-time performance.
Solution Approach 2:
The patent uses a multi-stage cascade where earlier stages perform partial classification with simpler features, and only regions passing through multiple stages undergo more complex analysis. This partial action approach achieves high accuracy for critical detections while reducing overall computational complexity by avoiding exhaustive analysis of all regions.
Data Source
AI summary
A system for detecting an object is provided. The system includes a depth image receiver that receives a depth image from a depth camera; a strong classifier that classifies an object region and a non-object region in the depth image based on a characteristic of an object; and an object detector that detects the classified object region, wherein the strong classifier comprises a plurality of weak classifiers which are cascade connected to each other and classifies the object region and the non-object region by passing the depth image through the weak classifiers, the characteristic of the object is extracted based on a center depth value of the depth image, and the plurality of the weak classifiers are generated through a training process for classifying positive training images among a multiple number of positive training images and a multiple number of negative training images.