A Novel Equation based Classifier for Detecting Human in Images

Mukherjee, Subra; Das, Kishore Kumar

doi:10.5120/12496-7272

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…Note that the face region does not imply the area of a face detection. In a real-world situation, there can be many scenes that have no frontal face; hence, the omega (Ω) shape detection method [17], [19]- [20] is employed, and the upper part of Ω indicates the head, and the lower region of Ω implies the shoulders. Through omega shape tracking, we extract the virtual face region; hence, we can then obtain E F even when the face of the subject cannot be seen.…”

Section: Environment Estimatorsmentioning

confidence: 99%

Uncooperative Person Recognition Based on Stochastic Information Updates and Environment Estimators

Kim¹,

Kim²,

Lee³

et al. 2015

ETRI J

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We address the problem of uncooperative person recognition through continuous monitoring. Multiple modalities, such as face, height, clothes color, and voice, can be used when attempting to recognize a person. In general, not all modalities are available for a given frame; furthermore, only some modalities will be useful as some frames in a video sequence are of a quality that is too low to be able to recognize a person. We propose a method that makes use of stochastic information updates of temporal modalities and environment estimators to improve person recognition performance. The environment estimators provide information on whether a given modality is reliable enough to be used in a particular instance; such indicators mean that we can easily identify and eliminate meaningless data, thus increasing the overall efficiency of the method. Our proposed method was tested using movie clips acquired under an unconstrained environment that included a wide variation of scale and rotation; illumination changes; uncontrolled distances from a camera to users (varying from 0.5 m to 5 m); and natural views of the human body with various types of noise. In this real and challenging scenario, our proposed method resulted in an outstanding performance.

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Section: Environment Estimatorsmentioning

confidence: 99%

Uncooperative Person Recognition Based on Stochastic Information Updates and Environment Estimators

Kim¹,

Kim²,

Lee³

et al. 2015

ETRI J

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show abstract

“…Human and Non-human objects). This task has become a quite challenging for researchers in computer vision area due to the fact that different objects tend to have different features which are usually used for object recognition [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, appropriate algorithms are needed to manage such these challenges to ensure the quality service of human detection algorithm (i.e. classification accuracy) [3,9].…”

Section: Introductionmentioning

confidence: 99%

Geometrical-based approach for robust human image detection

Al-Hazaimeh

Al-Nawashi

Saraee

2018

Multimed Tools Appl

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In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing based on human shape is one of the hot topic due to the wide applicability in real applications. In this paper, we present a new object classification approach. The new approach will use a simple and robust geometrical model to classify the detected object as human or non-human in the images. In the proposed approach, the object is detected. Then the detected object under different conditions can be accurately classified (i.e. human, non-human) by combining the features that are extracted from the upper portion of the contour and the proposed geometrical model parameters. A software-based simulation using Matlab was performed using INRIA dataset and the obtained results are validated by comparing with five state-of-art approaches in literature and some of the machine learning approaches such as artificial neural networks (ANN), support vector machine (SVM), and random forest (RF). The experimental results show that the proposed object classification approach is efficient and achieved a comparable accuracy to other machine learning approaches and other state-of-art approaches.

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“…Why menace IP-based video surveillance provides better picture quality and is also beneficial in terms of scalability and flexibility [1,4].…”

Section: Literature Surveymentioning

confidence: 99%

“…The specialists developed a swift reconnaissance camera that has the capability of recognizing the state of the scene that is being observed and furthermore gives warning or alert as the occasion happens this framework additionally gives security amid evening as it is having the capacity to give night vision. Night vision capability is attained by simply taking off infra-red (IR) filter from an ordinary webcam and thus can be used for night vision sensing with the help of IR Light Emitting Diode illuminator [4,5].…”

Section: Literature Surveymentioning

confidence: 99%

PIBOT: Reconnaissance and Live Streaming Frameworks utilizing Raspberry Pi

2018

IJRTER

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Abstract-In the present world, everybody is stressed over their wellbeing because of increment in wrongdoing rate. This has prompted an expansion in the significance of a reconnaissance framework. A framework is intended for persistent checking and furthermore, the framework gives live spilling. The framework can be sent to the wherever i.e. office, house and some remote place where individuals can't screen the specific place. The framework demonstrations like a Robot inside a neighborhood through Wi-Fi innovation utilizing Raspberry pi 2 B+ model. The live gushing is refined by utilizing a webcam interfaced with Raspberry Pi, it information gave is prepared by MJPEG (Motion Joint Photographic Experts Group) streamer and the robot is controlled through site page's made. The framework is customized utilizing python programming dialect.

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A Novel Equation based Classifier for Detecting Human in Images

Cited by 8 publications

References 19 publications

Uncooperative Person Recognition Based on Stochastic Information Updates and Environment Estimators

Uncooperative Person Recognition Based on Stochastic Information Updates and Environment Estimators

Geometrical-based approach for robust human image detection

PIBOT: Reconnaissance and Live Streaming Frameworks utilizing Raspberry Pi

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