Fast and accurate human detection for video applications using edgelets

Chowdhury, Mohammed Shahnewaz; Kuang, Ye Chow; Ooi, Melanie Po-Leen

doi:10.1109/iccaie.2010.5735050

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…All orientation maps are then searched for patches that contain similar orientation values using a constant 3×3 search window. The average difference between the patch and the template relate to its magnitude are calculated and compared to a degree of difference parameter p, as given in Equation (6). (6) where θ i and μ i are the ith orientations in the block of the patch and template respectively, G i is the ith magnitude in the block of the patch, G max is the maximum magnitude of the image, p is the maximum average difference parameter, default value used is 30° (π/6).…”

Section: Head and Shoulder Detectionmentioning

confidence: 99%

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Human Detection Using Gradient Maps and Golden Ratio

Su¹,

Fang²

2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Human detection is an important topic that can be used for many applications, it is mainly found in areas that required surveillance such as airports, casinos, factories, construction and mining sites. In this paper, a novel human detection method is introduced to extract the human figure from an input image without prior information or training. This method firstly uses a head and shoulder detection scheme based on curve detection with scaled gradient magnitude and orientation maps. It is then followed by a human body estimation scheme based on gap detection and golden ratio. Finally, the human figure is extracted through thresholding local gradient magnitude regions and horizontal filling. Tests on various images have shown that this method is capable of detecting and extracting human body figures robustly from different images.

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Section: Head and Shoulder Detectionmentioning

confidence: 99%

“…These goals may include people counting [3], face recognition [4] and behaviour recognition [5]. The advanced functions are generally used for high security environments such as airports and casinos, these systems tend to be expensive which equipped with high speed communication lines and powerful processors [6].…”

Section: Introductionmentioning

confidence: 99%

Human Detection Using Gradient Maps and Golden Ratio

Su¹,

Fang²

2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Real-time field sports scene classification using colour and frequency space decompositions

Kapela

McGuinness

O’Connor

2014

J Real-Time Image Proc

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This paper presents a novel approach to recognize a scene presented in an image with specific application to scene classification in field sports video. We propose different variants of the algorithm ranging from bags of visual words to the simplified real-time implementation, that takes only the most important areas of similar colour into account. All the variants feature similar accuracy which is comparable to very well-known image indexing techniques like SIFT or HoGs. For the comparison purposes, we also developed a specific database which is now available online. The algorithm is suitable in scene recognition task thanks to changes in speed and robustness to the image resolution, thus, making it a good candidate in real-time video indexing systems. The procedure features high simplicity thanks to the fact that it is based on the very well-known Fourier transform.

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Fast and accurate human detection for video applications using edgelets

Cited by 2 publications

References 13 publications

Human Detection Using Gradient Maps and Golden Ratio

Human Detection Using Gradient Maps and Golden Ratio

Real-time field sports scene classification using colour and frequency space decompositions

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