Neural Network based Approach for Recognition Human Motion using Stationary Camera

Modi, Rachana V.; Mehta, Tejas

doi:10.5120/3032-4110

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…SVM) for classification. Modi et al [28] developed a new algorithm for human motion recognition using stationary camera. Artificial Neural Network (i.e.…”

Section: Dalal and Triggsmentioning

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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“…SVM) for classification. Modi et al [28] developed a new algorithm for human motion recognition using stationary camera. Artificial Neural Network (i.e.…”

Section: Dalal and Triggsmentioning

confidence: 99%

Geometrical-based approach for robust human image detection

Al-Hazaimeh

Al-Nawashi

Saraee

2018

Multimed Tools Appl

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“…Instead of segmenting the silhouettes into 8x8 nonoverlapping blocks as shown in [9], radial features are directly calculated from the detected objects as shown in Figure 4. This captures the shape and a series of the features encodes the motion information.…”

Section: Feature Extractionmentioning

confidence: 99%

“…This approach fails to discriminate object with similar dispersednes. The authors in [9] used Artificial Neural Network approach for the classification of human motion on a still camera. It is noted that task to classify and identify objects in the video is difficult for human operator.…”

Section: Introductionmentioning

confidence: 99%

A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos

G.¹

2017

IJTSRD

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The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform.

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