Object Detection System Using Multi-Sensor Fusion and HOG Filtering
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Solution Overview
Problem
Existing systems for detecting objects from a moving platform in 3D-rich environments suffer from high false positive rates and increased computational burden, particularly when classifying objects against complex backgrounds, due to sensitivity to region of interest alignment and multiple gradient directions.
Innovation Solution
A multi-sensor system using visible, image intensifier, thermal, and radar sensors with a combination of detectors and filters, including AdaBoost or SVM classifiers that utilize contour-based matching and Histogram of Oriented Gradients (HOG) feature descriptors, applied in series to reduce false positives, and LWIR filtering to weed out obvious false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional classifiers with HOG feature descriptors are used, then false positive rate is reduced, but computational resources required increase significantly
Solution Approach 1:
The system segments the object detection task into multiple stages: initial detection using simplified features, followed by progressive refinement using more complex HOG features only on candidate regions. This segmentation allows the computationally intensive HOG classification to be applied selectively rather than to all image regions, reducing overall computational burden while maintaining low false positive rates.
Solution Approach 2:
The system applies full HOG feature computation only to regions identified as potential targets by preliminary detectors, rather than computing HOG features across the entire image. This partial application of the computationally expensive classifier reduces resource requirements while maintaining detection reliability for actual targets.
2Measurement precision
If exhaustive search over multiple positions and scales is performed, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary detection using computationally simple detectors to identify candidate object positions and scales before applying the more accurate but time-consuming HOG classifier. This preliminary action filters out non-target regions, allowing exhaustive multi-position and multi-scale search to be performed only on a small subset of candidate regions, thereby maintaining classification accuracy while dramatically reducing processing time.
3Difficulty of detecting and measuring
If image gradient-based features are used for classification, then object detection capability is improved, but false negative rate increases for objects against complex backgrounds
Solution Approach 1:
The system combines multiple feature types into a composite feature representation: contour-based features that capture object shape boundaries, HOG features that capture local gradient patterns, and contextual features that capture background relationships. This composite approach allows the classifier to leverage complementary information from different feature types, improving detection capability while reducing false negatives caused by gradient fragility in complex backgrounds.
Solution Approach 2:
The system dynamically adjusts feature computation parameters based on local image characteristics. In regions with complex backgrounds where gradient directions are highly variable, the system modifies HOG parameter settings (such as cell size, gradient binning, or weighting schemes) to reduce sensitivity to background noise, thereby maintaining detection capability while reducing false negatives.
Data Source
AI summary
The present invention relates to a system and method for detecting one or more targets belonging to a first class (e.g., moving and/or stationary people), from a moving platform in a 3D-rich environment. The framework described here is implemented using a number of monocular or stereo cameras distributed around the vehicle to provide 360 degrees coverage. Furthermore, the framework described here utilizes numerous filters to reduce the number of false positive identifications of the targets.