Top-Down and Bottom-Up Cues Based Moving Object Detection for Varied Background Video Sequences

Patel, Chirag; Garg, Sanjay; Zaveri, Tanish; Banerjee, Asim

doi:10.1155/2014/879070

Cited by 18 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Popular background subtraction techniques include frame differencing [ 23 , 24 ], Shadow removal [ 25 ], Gaussian mixture model(GMM) [ 26 ], and CNN-based background removal [ 27 ]. However, an algorithm for moving object detection without any background modeling was presented in [ 28 , 29 , 30 ], and the detailed procedure is given below.…”

Section: Proposed Frameworkmentioning

confidence: 99%

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

Patel

Labana

Pandya

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors.

show abstract

Section: Proposed Frameworkmentioning

confidence: 99%

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

Patel

Labana

Pandya

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, with the growth and propagation of IoT minimum cost and maximum proficiency, leads to bring the globe near to CIoT. With the cooperation of industries and organizations together, we anticipate challenges of internet of things could be dominated [49].…”

Section: Research Challenges and Open Issuesmentioning

confidence: 99%

“…Moreover, with aspect of CIoT, some of the challenges and issues like: synchronization between different cloud vendors, big data data analytics, object and face detection, intelligent surveillance, Standardizing cloud computing for IoT cloud-based services, balancing the differences in the infrastructure, unreliability in the security mechanisms, handling within different resources and components, are as the result the complexity of employing cloud computing with CIoT [45][46][47][48][49][50][51][52].…”

Section: Research Challenges and Open Issuesmentioning

confidence: 99%

The Application of Cognitive IoT for Smart Manufacturing

Abdurahman*,

Patel

2020

IJITEE

View full text Add to dashboard Cite

In recent times, novel paradigms based on cognitive manufacturing services are evolving. This paradigm shift is conveyed by integrating manufacturing assets with the latest and enhanced methods and technologies. However, today’s manufacturing systems are facing various challenges, that is the researcher believed that existing technologies and tools specifically, IoT, existing learning techniques and learning systems, lack enough cognitive based intelligence and cannot achieve the expected enhancements and smart manufacturing developments. In Light of this assessing the advances in manufacturing sectors was the major goal of this work and to identify the cuuent issues and challenches in manufacturing systems being Cognitive and or smart Afterward, we assessed the research challenges and open issues and facilitate knowledge accumulation in efficiently in the applications of Cognitive Internet of Things (CIoT) for smart manufacturing systems.

show abstract

“…Recently, background modeling and subtraction became the most popular technique for moving object detection in computer vision, such as object recognition and traffic surveillance [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

Yang

Zhang

Wang

et al. 2017

Advances in Multimedia

View full text Add to dashboard Cite

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

show abstract

Top-Down and Bottom-Up Cues Based Moving Object Detection for Varied Background Video Sequences

Cited by 18 publications

References 41 publications

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

The Application of Cognitive IoT for Smart Manufacturing

Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

Contact Info

Product

Resources

About