Night‐time pedestrian classification with histograms of oriented gradients‐local binary patterns vectors
Night‐time pedestrian classification with histograms of oriented gradients‐local binary patterns vectors
- New search for: Hurney, Patrick
- New search for: Waldron, Peter
- New search for: Morgan, Fearghal
- New search for: Jones, Edward
- New search for: Glavin, Martin
- New search for: Hurney, Patrick
- New search for: Waldron, Peter
- New search for: Morgan, Fearghal
- New search for: Jones, Edward
- New search for: Glavin, Martin
The use of night vision systems in vehicles is becoming increasingly common, not just in luxury cars but also in the more cost sensitive sectors. Numerous approaches using infrared sensors have been proposed in the literature to detect and classify pedestrians in low visibility situations. However, the performance of these systems is limited by the capability of the classifier. This paper presents a novel method of classifying pedestrians in far‐infrared automotive imagery. Regions of interest are segmented from the infrared frame using seeded region growing. A novel method of filtering the region growing results based on the location and size of the bounding box within the frame is described. This results in a smaller number of regions of interest for classification, leading to a reduced false positive rate. Histograms of oriented gradient features and local binary pattern features are extracted from the regions of interest and concatenated to form a feature for classification. Pedestrians are tracked with a Kalman filter to increase detection rates and system robustness. Detection rates of 98%, and false positive rates of 1% have been achieved on a database of 2000 images and streams of video; this is a 3% improvement on previously reported detection rates.
Document information
Night‐time pedestrian classification with histograms of oriented gradients‐local binary patterns vectors
IET Intelligent Transport Systems ; 9 , 1 ; 75-85
2015-02-01
11 pages
Article (Journal)
Electronic Resource
English
night‐time pedestrian classification , local binary pattern feature extraction , region of interest , high end luxury cars , automobiles , traffic engineering computing , Kalman filter , detection rates , pedestrians , far infrared automotive image streams , RoF , image classification , histogram of oriented gradient‐local binary pattern vectors , ROI , filtering theory , support vector machine classifier , image segmentation , histogram of oriented gradient feature extraction , infrared detectors , bounding box , night vision systems , low‐cost infrared sensors , captured infrared frame , feature extraction , filtering method , reduced false positive rate , support vector machines , seeded region growing
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