What are the computer vision algorithms for feature extraction used in video surveillance?
Best insight from top research papers
Computer vision algorithms for feature extraction in video surveillance include histogram-based methods. These methods involve extracting color histograms from images to analyze patterns and detect dominant colors . Additionally, techniques like the Hough line detector and morphological operators are utilized for automatic histogram detection and information extraction, aiding in the identification of axis, labels, and data frequency in histograms . Moreover, deep learning models such as Faster R-CNN are employed to detect elements in histograms and perform text recognition for structured data extraction in electronic devices . These algorithms collectively contribute to efficient feature extraction in video surveillance applications, enabling the analysis of visual data for security and monitoring purposes.
Answers from top 5 papers
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| Papers (5) | Insight |
|---|---|
| The paper discusses color histogram extraction, edge map analysis for pattern detection, and dominant color identification as image feature extraction methods, not specifically focusing on video surveillance algorithms. | |
| The Faster R-CNN model is utilized for target detection in histograms, enabling effective feature extraction in video surveillance through deep learning methods. | |
01 Jan 2018 2 Citations | Not addressed in the paper. |
| The paper discusses color histogram extraction, edge map analysis for pattern detection, orientation histogram extraction, and dominant color identification as image feature extraction methods, relevant for video surveillance in computer vision. | |
4 Citations | The paper proposes an automatic histogram detection system using Hough line detector and Morphological operator for feature extraction, not specifically focusing on computer vision algorithms for video surveillance. |
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